{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from matplotlib import pyplot as pl\n", "\n", "pl.rcParams['figure.figsize'] = (8, 5)\n", "pl.rc('xtick', labelsize=16) \n", "pl.rc('ytick', labelsize=16) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exemplos de operações básicas em Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vamos criar um vetor $v$ de valores igualmente espaçados em um dado intervalo $[x_{min}, x_{max}-1)$: $v = [x_{min},x_{min}+\\Delta x,\\ldots, x_{max} - \\Delta x, x_{max}]$. Para um exemplo mais concreto, tomaremos $x_{min} =0$, $x_{max} = 1$ e $\\Delta x = 0.1$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Criamos um array que contém os elementos do intervalo [0.0,1.0] com espaçamento Delta_x = 0.1\n", "[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]\n", "\n", "\n", "Note que se não definirmos o espaçamento entre os valores, o espaçamento padrão do Python é Delta_x = 1!\n", "[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n", "\n", "\n", "Obs: se não colocamos os decimais (i.e. 0 e 10 ao invés de 0.0 e 10.0), o Python gera um array com valores inteiros! \n", "\n", "[0 1 2 3 4 5 6 7 8 9]\n", "\n", "\n", "Abaixo mostramos como o Python interpreta os elementos dos arrays u e x:\n", "(dtype('float64'), dtype('int64'))\n" ] } ], "source": [ "print('Criamos um array que contém os elementos do intervalo [0.0,1.0] com espaçamento Delta_x = 0.1')\n", "v = np.arange(0.0,1.0,0.1)\n", "print(v)\n", "print('\\n')\n", " \n", "print('Note que se não definirmos o espaçamento entre os valores, o espaçamento padrão do Python é Delta_x = 1!')\n", "u = np.arange(0.0,10.0)\n", "print(u)\n", "\n", "print('\\n')\n", "print('Obs: se não colocamos os decimais (i.e. 0 e 10 ao invés de 0.0 e 10.0), o Python gera um array com valores inteiros! \\n')\n", "\n", "x = np.arange(0,10)\n", "print(x)\n", "\n", "print('\\n')\n", "print('Abaixo mostramos como o Python interpreta os elementos dos arrays u e x:')\n", "\n", "print(np.dtype(u[2]), np.dtype(x[2]))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Abaixo criamos um vetor com 10 elementos no intervalo de 0 a 5, linearmente espaçados.\n", "Neste caso, o espaçamento entre os elementos é feito automaticamente pelo Python.\n", "\n", "\n", "[0. 0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n", " 3.33333333 3.88888889 4.44444444 5. ]\n", "\n", "\n", "Abaixo criamos um vetor com 100 elementos no intervalo de 0 a 15, linearmente espaçados.\n", "\n", "\n", "[ 0. 0.15151515 0.3030303 0.45454545 0.60606061 0.75757576\n", " 0.90909091 1.06060606 1.21212121 1.36363636 1.51515152 1.66666667\n", " 1.81818182 1.96969697 2.12121212 2.27272727 2.42424242 2.57575758\n", " 2.72727273 2.87878788 3.03030303 3.18181818 3.33333333 3.48484848\n", " 3.63636364 3.78787879 3.93939394 4.09090909 4.24242424 4.39393939\n", " 4.54545455 4.6969697 4.84848485 5. 5.15151515 5.3030303\n", " 5.45454545 5.60606061 5.75757576 5.90909091 6.06060606 6.21212121\n", " 6.36363636 6.51515152 6.66666667 6.81818182 6.96969697 7.12121212\n", " 7.27272727 7.42424242 7.57575758 7.72727273 7.87878788 8.03030303\n", " 8.18181818 8.33333333 8.48484848 8.63636364 8.78787879 8.93939394\n", " 9.09090909 9.24242424 9.39393939 9.54545455 9.6969697 9.84848485\n", " 10. 10.15151515 10.3030303 10.45454545 10.60606061 10.75757576\n", " 10.90909091 11.06060606 11.21212121 11.36363636 11.51515152 11.66666667\n", " 11.81818182 11.96969697 12.12121212 12.27272727 12.42424242 12.57575758\n", " 12.72727273 12.87878788 13.03030303 13.18181818 13.33333333 13.48484848\n", " 13.63636364 13.78787879 13.93939394 14.09090909 14.24242424 14.39393939\n", " 14.54545455 14.6969697 14.84848485 15. ]\n", "\n", "\n", "Tamanho do vetor v100:\n", "100\n", "\n", "\n", "Abaixo criamos um vetor com 10 elementos no intervalo de 0 a 5 igualmente espaçados em escala logaritmica\n", "\n", "\n", "[1.00000000e+00 3.59381366e+00 1.29154967e+01 4.64158883e+01\n", " 1.66810054e+02 5.99484250e+02 2.15443469e+03 7.74263683e+03\n", " 2.78255940e+04 1.00000000e+05]\n" ] } ], "source": [ "# Diferente da função np.arange(), as funções abaixo criam um vetor com números igualmente espaçados para uma \n", "# quantidade definida de elementos.\n", "\n", "# FUNÇÃO np.linspace:\n", "print('Abaixo criamos um vetor com 10 elementos no intervalo de 0 a 5, linearmente espaçados.')\n", "print('Neste caso, o espaçamento entre os elementos é feito automaticamente pelo Python.')\n", "print('\\n')\n", "\n", "v10 = np.linspace(0,5,10)\n", "print(v10)\n", "print(\"\\n\")\n", "\n", "print('Abaixo criamos um vetor com 100 elementos no intervalo de 0 a 15, linearmente espaçados.')\n", "print('\\n')\n", "v100 = np.linspace(0.,15.,100)\n", "print(v100)\n", "print('\\n')\n", "print('Tamanho do vetor v100:')\n", "print(len(v100))\n", "print('\\n')\n", "\n", "# FUNÇÃO np.logspace\n", "\n", "print('Abaixo criamos um vetor com 10 elementos no intervalo de 0 a 5 igualmente espaçados em escala logaritmica')\n", "print('\\n')\n", "u10 = np.logspace(0,5,10)\n", "print(u10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vamos agora passar por alguns exemplos de operações básicas." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y = np.zeros(len(u10))\n", "pl.plot(v10, y, 'o') #'o' é o formato do ponto\n", "pl.title(\"linspace\") #coloca título no gráfico\n", "pl.show()\n", "\n", "pl.plot(u10, y, 'go') #'go' é a cor (g = green) e o formato do ponto\n", "pl.title(\"logspace\")\n", "pl.show()\n", "\n", "# Observação: o pl.show() é necessário quando você quer reproduzir, no Jupyter, dois gráficos na mesma saída.\n", "# Se omitimos pl.show() para o primeiro gráfico, apenas o último será mostrado no output do Jupyter\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 2.0, 3.0, 6.0, 8.0, 9.0, 10.0]\n", "\n", "\n", "[0.0, 2.0, 3.0, 6.0, 8.0, 9.0, 10.0, 0.0, 2.0, 3.0, 6.0, 8.0, 9.0, 10.0, 0.0, 2.0, 3.0, 6.0, 8.0, 9.0, 10.0]\n", "\n", "\n", "[ 0. 2. 3. 6. 8. 9. 10.]\n", "\n", "\n", "[ 0. 6. 9. 18. 24. 27. 30.]\n" ] } ], "source": [ "# Suponha que queremos criar um array com elementos já conhecidos:\n", "\n", "lista = [0.,2.,3.,6.,8.,9.,10.]\n", "\n", "print(lista)\n", "print('\\n')\n", "\n", "# Este elemento não é interpretado pelo Python como um array, mas sim como uma lista.\n", "# Esperaríamos 3*lista nos retorne 3 vezes cada entrada. Mas para listas o Python concatena este vetor três vezes, \n", "# isto é, 3*lista = (lista,lista,lista):\n", "\n", "print(3*lista)\n", "print('\\n')\n", "# Devemos impor que o objeto é um array\n", "\n", "vec = np.array(lista)\n", "\n", "print(vec)\n", "print('\\n')\n", "\n", "# Então podemos executar operações sobre os elementos do array!\n", "print(3*vec)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 3 6 9 12 15 18 21 24 27]\n", "[0 1 2 3 4 5 6 7 8 9]\n", "\n", "\n", "Abaixo a soma dos arrays: vec1+vec2\n", "[ 0 4 8 12 16 20 24 28 32 36]\n", "\n", "\n", "Abaixo a soma dos elementos dos arrays:\n", "135\n", "45\n", "\n", "\n", "Abaixo a multiplicação dos arrays: vec1*vec2\n", "[ 0 3 12 27 48 75 108 147 192 243]\n" ] } ], "source": [ "# Vamos somar arrays:\n", "vec1 = 3*np.arange(0,10)\n", "print(vec1)\n", "vec2 = np.arange(0,10)\n", "print(vec2)\n", "print('\\n')\n", "\n", "soma12 = vec1+vec2\n", "print('Abaixo a soma dos arrays: vec1+vec2')\n", "print(soma12)\n", "print('\\n')\n", "\n", "soma1 = np.sum(vec1)\n", "soma2 = np.sum(vec2)\n", "print('Abaixo a soma dos elementos dos arrays:')\n", "print(soma1)\n", "print(soma2)\n", "print('\\n')\n", "\n", "print('Abaixo a multiplicação dos arrays: vec1*vec2')\n", "mult12 = vec1*vec2\n", "print(mult12)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Abaixo um vetor no intervalo [0,pi) com os elementos espaçados por Delta_theta = 0.2:\n", "[0. 0.2 0.4 0.6 0.8 1. 1.2 1.4 1.6 1.8 2. 2.2 2.4 2.6 2.8 3. 3.2 3.4\n", " 3.6 3.8 4. 4.2 4.4 4.6 4.8 5. 5.2 5.4 5.6 5.8 6. 6.2]\n", "\n", "\n", "Abaixo o cosseno desses ângulos:\n", "[ 1. 0.98006658 0.92106099 0.82533561 0.69670671 0.54030231\n", " 0.36235775 0.16996714 -0.02919952 -0.22720209 -0.41614684 -0.58850112\n", " -0.73739372 -0.85688875 -0.94222234 -0.9899925 -0.99829478 -0.96679819\n", " -0.89675842 -0.79096771 -0.65364362 -0.49026082 -0.30733287 -0.11215253\n", " 0.08749898 0.28366219 0.46851667 0.63469288 0.77556588 0.88551952\n", " 0.96017029 0.9965421 ]\n", "\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0,0.5,u'$cos(\\theta)$')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Abaixo um vetor no intervalo [0,pi) com os elementos espaçados por Delta_theta = 0.2:')\n", "angulos = np.arange(0.,2*np.pi,0.2)\n", "print(angulos)\n", "print('\\n')\n", "\n", "print('Abaixo o cosseno desses ângulos:')\n", "cosseno = np.cos(angulos)\n", "print(cosseno)\n", "print('\\n')\n", "\n", "pl.plot(angulos, cosseno)\n", "pl.title('Cosseno')\n", "pl.xlabel(r'$\\theta$') # O r funciona para que o Python interprete o texto entre cifrões $ como o ambiente \n", "pl.ylabel(r'$cos(\\theta)$') # matemático (mathmode) do Latex. Os mesmos códigos em tex funcionam aqui.\n", "pl.show()\n", "\n", "# Se omitimos o r, o Python não reconhece os comandos latex.\n", "novos_angulos = np.arange(0,6*np.pi,0.2)\n", "pl.plot(novos_angulos,np.cos(novos_angulos),'r')\n", "pl.xlabel('$\\theta$') \n", "pl.ylabel('$cos(\\theta)$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Integrando uma função $f(z)$ com a soma cumulativa" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('entrada de z mais proxima de 0.1:', 20)\n", "('checando:', 0.1)\n", "('valor da integral de x^2 de 0 a 0.1:', 0.0003587500000000001)\n", "('valor esperado:', 0.00033333333333333343)\n" ] } ], "source": [ "## Vamos supor que H(z,parâmetros) é descrito por uma função f(z), e.g. z^2.\n", "# Vamos supor uma função f(z), e.g. z^2.\n", "dz = 0.005\n", "z = np.arange(0.0,2,dz) # criamos um array no intervalo [0.0,2) com elementos espaçados por dz\n", "f = z**2\n", "\n", "integral = dz*np.cumsum(f)\n", "\n", "print('entrada de z mais proxima de 0.1:', np.argsort(np.abs(z-0.10))[0]) \n", "print('checando:', z[20])\n", "print('valor da integral de x^2 de 0 a 0.1:', integral[20])\n", "print('valor esperado:', z[20]**3/3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolando um array f sobre um array valores z " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Importando nossos dados\n", "# O parâmetro unpack quando tem valor \"True\", já separa cada coluna da tabela de dados nos arrays redshift (coluna 1\n", "# da tabela), dist_mod (coluna 2), dist_err (coluna 3), etc.\n", "redshift, dist_mod, dist_err = np.loadtxt(\"Dados_SN_Union2.dat\", unpack = True)\n", "\n", "# A função interp1d da classe scipy interpolate interpola funções unidimensionais.\n", "# Vamos importá-la:\n", "from scipy.interpolate import interp1d\n", "# Mais informações: documentação scipy do python \"scipy.interpolate.interp1d\"\n", "\n", "\n", "######################################\n", "\n", "\n", "# Vamos supor que queremos interpolar uma função f(z) sobre os valores da nossa lissa de redshift.\n", "# Para a interpolação ir de 0.0 ao valor máximo de redshift, vamos acrescentar o valor z=0 ao nosso array.\n", "\n", "z = np.append(redshift,0) # adicionamos o valor 0 ao array\n", "z = np.sort(z) # organizamos seus valores em ordem crescente\n", "\n", "f = z**2\n", "\n", "# Temos então um \"conjunto de dados\" f e z.\n", "pl.scatter(z,f,label='\"Dados\"')\n", "\n", "# Vamos agora interpolar essa lista de elementos para obtermos uma função:\n", "f_interp = interp1d(z,f)\n", "\n", "# Temos agora uma função que interpola a curva f = z^2 para os valores de z desejados.\n", "# Vamos plotar a função obtida:\n", "pl.plot(z,f_interp(z),'r',lw='2',label='Curva interpolada')\n", "pl.legend(fontsize=18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plots \"triangulares\" (corner plot)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Suponha que temos uma teoria a_1 x + a_2 sin(x) para a variável x, com parâmetros a_i.\n", "# Vamos criar esse modelo:\n", "def teoria(a,x):\n", " return a[0]*x + a[1]*np.sin(x) \n", "\n", "# Nosso vetor de parâmetros fiduciais (i.e. aqueles que acreditamos serem os valores corretos de a_i) será\n", "a_fiducial = np.array([0.5,1.0])\n", "\n", "# Vamos agora supor que tenhamos dados para essa variável x.\n", "# Abaixo, criamos um conjunto de dados simulados com a teoria? \n", "x = np.arange(0.,2.,0.01)\n", "ruido = 0.03*np.ones(x.shape)\n", "teoria = teoria(a_fiducial,x)\n", "\n", "# Nossos dados serão então a nossa teoria nos valores fiduciais dos parâmetros + um ruído Gaussiano para simular \n", "# a \"aleatoriedade\" das medidas\n", "dados = teoria + np.random.normal(0.0,ruido) \n", "# A função np.random.normal(mu,sigma) gera um array cujos elementos seguem uma distribuição normal com média mu e\n", "# desvio padrão sigma.\n", "\n", "pl.scatter(x,dados,marker='.')\n", "pl.plot(x,teoria,'r-',lw=1.5,label=r'$\\frac{x}{2}+\\sin(x)$')\n", "pl.legend(fontsize=15)\n", "pl.xlabel('x',fontsize=15)\n", "pl.ylabel('Dados',fontsize=15)\n", "pl.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Suponho que esses dados possuíam erros e que com isso realizamos a mesma atividade das supernovas: determinamos os\n", "# a probabilidade, dadas as nossas medidas, de obtermos certos valores para a_1 e a_2.\n", "# Estou importanto uma matriz qualquer que contém a probabilidade do parâmetro a_1 (linha) e do a_2 (coluna).\n", "\n", "# Vamos supor que os valores teste para a_1 e a_2 foram\n", "a1 = np.arange(0.0,1.0,0.01)\n", "a2 = np.arange(0.5,3.5,0.01)\n", "\n", "# e com isso obtivemos uma matriz com as probabilidades de cada valor\n", "prob_a = np.loadtxt('teste.dat')\n", "\n", "# As probabilidades marginalizadas de a_1 e a_2 serão, respectivamente, a soma da probabilidade conjunta \n", "# sobre a_2 e a_1. (Veja, por exemplo: https://en.wikipedia.org/wiki/Marginal_distribution).\n", "# Podemos usar a função np.sum para realizar esta tarefa. Os exemplos contidos na documentação do Python\n", "# \"numpy.sum\" exemplifica bem como essa função opera.\n", "\n", "prob_marginal_a1= np.sum(prob_a,axis=1) #<- probabilidade dos valores de a_1 (dados pelo array a1)\n", "prob_marginal_a2= np.sum(prob_a,axis=0) #<- probabilidade dos valores de a_2 (dados pelo array a2)\n", "\n", "# Com essa análise, conseguimos obter o valor esperado e o erro para as nossas variáveis.\n", "# Vamos supor que os valores obtidos foram\n", "media_a2 = 2.15\n", "erro_a2 = 0.08\n", "media_a1 = 0.38\n", "erro_a1 = 0.03" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((array([-0.02, 0. , 0.02, 0.04, 0.06]),\n", " ),\n", " (array([1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6]),\n", " ))" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Aqui disponibilizo uma forma de reproduzir gráficos como aqueles\n", "\n", "# A linha abaixo configura o tamanho total da imagem (conjunto dos três subplots)\n", "pl.rcParams['figure.figsize'] = (10, 8)\n", "\n", "# Subplot 1:\n", "pl.subplot(2,2,1)\n", "pl.plot(a1,prob_marginal_a1,'k',lw=1.5,label=r'Marginalizada')\n", "pl.axvline(x=media_a1,ls=':',c='k')\n", "pl.ylabel(r'$P(a_1)$', fontsize = 15)\n", "pl.title(r'$\\langle a_1 \\rangle = 0.38 \\pm 0.03$ [unidades]', fontsize = 16)\n", "pl.legend(loc=\"upper left\")\n", "pl.xlim([0.6,0.2])\n", "pl.xticks(fontsize = 15), pl.yticks(fontsize = 15)\n", "\n", "# Subplot 2:\n", "pl.subplot(2,2,3)\n", "pl.contour(a1,a2,np.transpose(prob_a))\n", "pl.axvline(x=media_a1,ls=':',c='k')\n", "pl.axhline(y=media_a2,ls=':',c='k')\n", "pl.ylabel(r'$a_2$', fontsize = 15)\n", "pl.xlabel(r'$a_1$', fontsize = 15)\n", "pl.ylim([1.5,2.5])\n", "pl.xlim([0.6,0.2])\n", "\n", "# Subplot 3:\n", "pl.subplot(2,2,4)\n", "pl.plot(prob_marginal_a2,a2,'k',lw=1.5,label=r'Marginalizada')#$\\sum_{H_0}P(H_0,q_0|\\mu)$\n", "pl.axhline(y=media_a2,ls=':',c='k')\n", "pl.xlabel(r'$P(a_2)$', fontsize = 15)\n", "pl.title(r'$\\langle a_2 \\rangle = 2.15 \\pm 0.08$ [unidades]', fontsize = 16)\n", "pl.ylim([1.5,2.5])\n", "pl.legend(loc=\"lower right\")\n", "pl.xticks(fontsize = 15), pl.yticks(fontsize = 15)\n", "\n", "# Para salvar seu resultado, descomente (tire o símbolo #) da linha abaixo pl.savefig(). Se quiser mudar a \n", "# extensão do arquivo, basta trocar .pdf por .png, .jpg, etc.\n", "\n", "#pl.savefig('jointanalysis.pdf') " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }