diff --git a/Entrega_SemprumG.ipynb b/Entrega_SemprumG.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..81337b877c98d5165d675cc2c29905889f6f2a72 --- /dev/null +++ b/Entrega_SemprumG.ipynb @@ -0,0 +1,1304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Optimización y procesamiento de imágenes (numpy-scipy)\n", + "Gerardo Semprúm.\n", + "\n", + "Universidad Central de Venezuela.\n", + "\n", + "Módulo de datos.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "El proyecto descrito es un primer paso a lo que seria la caracterización y procesamiento de datos e imagenes. El problema en cuestrión consta de una imagen que refleja muchisimas estrellas cada una con una luminosidad caracteristica. Este proyecto espera poder optimizar dichas imagenes y crear diferentes gaussianas de donde sea mas sencillo el procesamiento de datos." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Importamos las librerias que necesitaremos para trabajar en nuestro código. Dichas librerias fueron: **numpy**, **scipy** y **matplotlib**. Además de traer una herramienta de optimización contenida en scipy, llamada leastsq.\n", + "\n", + "Por otro lado, aplicamos la libreria **sys** con la finalidad de darle a python un camino que seguir e importamos \"cmap\" de matplotlib que será util para procesar las imágenes." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy\n", + "import matplotlib.pyplot as plt \n", + "from scipy.optimize import leastsq " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys \n", + "sys.path.append(\"./data/zapatocaImage.jpeg\")\n", + "from matplotlib import cm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "En la primera linea del codigo, traemos la imagen desde una carpeta destino, la cual automaticamente es procesada como un array. Ese array estará compuesto de 3 matrices, una tras otra, correspondiente a los canales R,G,B.\n", + "\n", + "Podemos comprobar que es un array debido a que le pedimos a python que nos imprima la forma (.shape()) de nuestra imagen y obtenemos un array de 3 matrices cada una con 789 filas y 1184 columnas" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(789, 1184, 3)\n" + ] + }, + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc837567a58>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "stars = plt.imread(\"./data/zapatocaImage.jpeg\")\n", + "print(stars.shape)\n", + "plt.imshow(stars)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Separamos dicho array, asignado cada matriz a una variable \"color\" asignado asi los valores de rojo, verde y azul respectivamente. Por ejemplo, si pedimos la forma (.shape()) de cualquiera de nuestro nuevos arreglos, obtenemos una matriz 2D." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#Asignando un cada color a un arreglo respectivo \n", + "red = stars[:,:,0]\n", + "green = stars[:,:,1]\n", + "blue = stars[:,:,2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para juntar el trio de matrices, podria establecerse una función \"suma\", en donde se multiplique cada color por una constante y despues se sumen entre sÃ.\n", + "\n", + "**Uso de la constante**: Al transformar una imagen colorida a una escala de grises debe multiplicarse cada matriz correspondiente a los colores (R,G,B) por una constante propia de cada color para evitar la perdida de luminosidad de la imagen. Puede leerse mas al respecto en la referencia anexada:\n", + "[Convirtiendo color a escala de grises](https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale)\n", + "\n", + "Para sumar y multiplicar cada matriz \"color\" con su constante asignada, usamos la función de numpy.dot() la cual devuelve el producto punto entre nuestra matriz principal \"stars\" y una lista de con las constantes de cada color(R,G,B).\n", + "\n", + "También es necesario aplicarle un mapeo de colores en escala de grises (cmap = \"gray\") para que la imagen adopte su escala de grises (este ultimo paso se repite en muchas imagenes a futuro por lo que no será explicado nuevamente)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(789, 1184)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gray = np.dot(stars[...,:3], [0.299, 0.587, 0.114])\n", + "plt.imshow(gray, cmap=\"gray\")\n", + "print(gray.shape)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Siguiendo el procedimiento, recortamos una sección de la imagen para aislar una estrella de nuestra preferencia para realizar una gaussiana de su luminosidad y la optimización de dicha gaussiana. \n", + "\n", + "Para cortar la imagen solo es necesario crear una función (en este caso: **estrella1**) que sea un subconjunto de **gray** (nuestra imagen en escala de grises).\n", + "\n", + "Para recortar una parte de la imagen, definimos la función de la siguiente manera:\n", + "$$estrellaX = gray[val_{y0}:val_{y1} , val_{x0}:Val_{x1}]$$\n", + "\n", + "Siendo el eje \"Y\" el eje vertical y el eje \"X\" el eje horizontal. Finalmente solo pedimos imprimir la estrella deseada.\n", + "\n", + "Para manejar con mas facilidad el array que contiene a nuestra estrella, usamos la variable \"st1\" (diminutivo de \"star1\"). Las estrellas siguientes usarán la misma etiqueta con diferente numeración" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(30, 70)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Recortando nuestra primera estrella \n", + "estrella1 = gray[540:570, 630:700]\n", + "plt.imshow(estrella1, cmap=\"gray\")\n", + "\n", + "st1 = np.asarray(estrella1)\n", + "print(st1.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Definimos una función gaussiana para modelar nuestro sistema de la forma:\n", + "$$Gauss = Ae^{-\\frac{(x-x_0)^2 + (y-y_0)^2}{2C^2}}+ B $$\n", + "\n", + "**Consejo para aplicar la función:** la función construida depende de varios parámetros además de Xe Y. El parametro 0 (A) es la amplitud deseada, el paramtro 1 (B) es una constante aditiva, el parametro 2 (C) es la desviación estandar, el parametro 3 (X0) es la posición inicial en X y el parametro 4 (Y0) es la posición inicial en Y." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def gauss2D(params,x, y):\n", + " exponente = -((x-params[3])**2 + (y-params[4])**2) / (2*params[2]**2)\n", + " z = params[0]* np.exp(exponente) + params[1]\n", + " return z\n", + "#params[0]: constante de amplitud\n", + "#params[1]: constante aditiva\n", + "#params[2]: desviacion estandar\n", + "#params[3]: X0\n", + "#params[4]: Y0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construmos un primer arreglo para modelar la primera estrella, se hace un arreglo de 70 números en X y 30 números en Y con la intención de recrear la imágen anterior.\n", + "\n", + "Se realiza un .meshgrid() debido a que hace un arreglo de X-Y para evaluaciones vectorizadas de una escala X-Y sobre X-Y grids dando un arreglo de coordenada unidimensional." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#Definiendo los valores modelo de nuestra primera estrella (Gaussiana 2D)\n", + "x = np.arange(0,70,1)\n", + "y = np.arange(0,30,1)\n", + "\n", + "xx , yy = np.meshgrid(x,y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Por aproximación damos unos parametros para tratar de simular nuestra estrella, asignamos la primera estrella a la variable zz haciendo uso de la función antes descrita e imprimimos" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82e01d2b0>" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "params = [150,1,5,33,14]\n", + "zz = gauss2D(params,xx,yy)\n", + "plt.imshow(zz,cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Definiendo una función error, la cual es una función comparativa que trabajará enfrentando los valores reales y los valores modelados. Dicha función depende de tpl(que es una tupla con los parametros que se usaron para calcular el modelo), x, y e z.\n", + "\n", + "Definimos dentro de la función a zmodel, que es nuestra función gauss2D antes descrita que depende de la tupla, x e y. \n", + "\n", + "Finalmete los errores (\"errors2D\") serán la resta entre los valores modelo y los valores reales:\n", + "$$errors2D = Z_{model} - Z$$\n", + "\n", + "Al realizar dicha resta, obtenemos una nueva matriz. De donde asignaremos a F(filas) y C(columnas) a los elementos de esa matriz respectivamente. \n", + "\n", + "Luego de eso aplicaremos un reshape a la matriz del tipo (F*C) con el objetivo de que sea una función general para todas las estrellas a trabajar.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "#Definimos la función error:\n", + "def ErrorGauss2D(tpl,x,y,z):\n", + " zmodel = gauss2D(tpl,x,y)\n", + " errors2D = zmodel - z\n", + " F, C = errors2D.shape\n", + " return errors2D.reshape(F*C)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Damos un \"chute inicial\" (p1) y finalmente \"best1\" solo será una tupla con los mejores valores calculados entre el modelo y los valores reales. De donde, leastsq() es una función que minimiza la suma de cuadrados de un set de ecuaciones.\n", + "\n", + "Notando que el \"chute inicial\" (p1) está definimo como los params usados en el modelo de la primera estrella. Estos chutes iran cambiando según la estrella.\n", + "\n", + "Para obtener \"best1\", llamamos a nuestra función de Error, evaluada en p1 y con argumentos xx,yy(matriz modelo) y st1(primera estrella)." + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[149.49087839 116.36309968 4.88921373 32.41971607 12.85314581]\n" + ] + } + ], + "source": [ + "p1 = params \n", + "best1,suss = leastsq(ErrorGauss2D, p1, args=(xx,yy,st1))\n", + "print(best1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solo queda graficar nuestra gaussiana optimizada (\"st1_model_opt\"), para eso, usamos la función modelo evaluada en los valores de \"best1\" y en el meshgrid de xx,yy." + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82dff9c88>" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "st1_model_opt = gauss2D(best1,xx,yy)\n", + "plt.imshow(st1_model_opt,cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Trabajando con la segunda estrella:**\n", + "\n", + "Recortamos la estrella que deseamos trabajar como fué antes explicado y asignamos los pixeles de dicha estrella como un array a st2 (np.asarray())" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(40, 30)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Recortando la segunda estrella\n", + "estrella2 = gray[360:400,430:460]\n", + "plt.imshow(estrella2, cmap=\"gray\")\n", + "st2 = np.asarray(estrella2)\n", + "print(st2.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Definiendo los valores modelo de nuestra segunda estrella (Gaussiana 2D). Creamos un arreglo de (30,40) que simule nuestra segunda estrella. Usando x2, y2, xx2, yy2 para no confudir valores con la estrella anterior." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "x2 = np.arange(0,30,1)\n", + "y2 = np.arange(0,40,1)\n", + "\n", + "xx2 , yy2 = np.meshgrid(x2,y2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "También generamos una segunda lista de parametros para la estrella en cuestión (params2). Y evaluamos nuesta función gaussiana bajo el nombre de z2, en donde, aplicamos a la función los valores de interes para esta estrella. " + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82ea16630>" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AXwFTwM6I+NJIOzBAkp4E7qG3gvQE8EXgH4GngZ+ntyL4/ojIg/GxJulu4LvAK8ClpvlheuOKiX5tbfgbbbPEA22zxKEwSxwKs8ShMEscCrPEoTBLHAqzxKEwS/4XCMh34bRVDSYAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "params2 = [5,5,3.5,20,17]\n", + "zz2 = gauss2D(params2,xx2,yy2)\n", + "plt.imshow(zz2,cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Definimos un \"segundo chute\" (p2) que estára definido por \"params2\". Calculamos los mejores valores posibles con \"best2\" e imprimimos dichos valores." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[130.78142434 108.14028778 2.20724331 20.33084866 16.81819746]\n" + ] + } + ], + "source": [ + "p2 = params2\n", + "best2,suss = leastsq(ErrorGauss2D, p2, args=(xx2,yy2,st2))\n", + "print(best2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ahora graficamos nuestra segunda estrella (\"st2_model_opt\") aplicando los valores de \"best2\" a la función gaussiana junto con los parametros xx2,yy2." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82ef02438>" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "st2_model_opt = gauss2D(best2,xx2,yy2)\n", + "plt.imshow(st2_model_opt,cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Trabajando con la tercera estrella:**\n", + "\n", + "Los pasos explicados anteriormente aplican para la tercera estrella. De donde se hara un array de (30,20), definiremos un modelo con zz3 que dependerá de \"params3\" además de xx3,yy3." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(30, 20)\n" + ] + }, + { + "data": { + "image/png": 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CxqnPoyLy86ontJQ2yk5Vl57a6pdelSrCfA7Auqbv1xbPVUZVzxVf6wAOItAHvAIzi22di6/1KifTbr/0qlQR5ncBbBSRu0RkEMCDAA5VMA8AgIgMicgti48BbEPv9AE/BGBX8XgXgNcrnEvP90uvoqfJnIjsAfAWgBqA/ap6sux5NBkDcLC40V8/gJdU9c2yJyEiLwPYCmBURM4CeBzAPgCvicjDaKw23FnxfLb2cr90fgJIbvAPQHKDYSY3GGZyg2EmNxhmcoNhJjcYZnKDYSY3/gum6MehTS85DAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Tercera estrella\n", + "estrella3 = gray[230:260,210:230]\n", + "plt.imshow(estrella3, cmap=\"gray\")\n", + "st3 = np.asarray(estrella3)\n", + "print(st3.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "#Definiendo los valores modelo de nuestra tercera estrella (Gaussiana 2D)\n", + "x3 = np.arange(0,20,1)\n", + "y3 = np.arange(0,30,1)\n", + "\n", + "xx3 , yy3 = np.meshgrid(x3,y3)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82ebb0f28>" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "params3 = [3,4,2,8,13]\n", + "zz3 = gauss2D(params3,xx3,yy3)\n", + "plt.imshow(zz3,cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Damos como siempre el \"chute inicial\" para la tercera estrella (p3), calculamos los mejores valores posibles (\"best3\") y finalmente graficamos nuestra gaussiana optimizada." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[131.64909249 112.00306687 1.71925599 7.88240793 13.57465711]\n" + ] + } + ], + "source": [ + "p3 = params3\n", + "best3,suss = leastsq(ErrorGauss2D, p3, args=(xx3,yy3,st3))\n", + "print(best3)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82eb1a9e8>" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "st3_model_opt = gauss2D(best3,xx3,yy3)\n", + "plt.imshow(st3_model_opt,cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como ultimo procedimiento de este proyecto, calcularemos la anchura a media altura(FWHM) para cada una de nuestras estrellas tratadas.\n", + "Para eso necesitamos la desviación estandar contenida en nuestras graficas optimizadas, para eso usaremos los valores contenido en las funciones \"best\".\n", + "\n", + "Definimos la función FWHM:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "def FWHM(f):\n", + " FWHM = 2*np.sqrt(2*np.log(2))*f\n", + " return FWHM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Presentamos asi los FWHM de cada estrella 1, 2 y 3 respectivamente." + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11.513218489642778" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FWHM(best1[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.197660781112112" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FWHM(best2[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.048538475508802" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FWHM(best3[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como antesala a el proyecto antes mencionado, trabajamos con bandas de estrellas y generando gaussianas de una dimesión(1D). Se adjunta lo realiado pero no se explicará a detalle debido a que en escencia es el mismo codigo que se vio anteriormente." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Para una banda de la primera estrella:**" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 70)\n" + ] + }, + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82e47a0f0>" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Gaussiana de 1D. Intensidad Luminosa\n", + "#Cortando Las filas centrales de la estrella:\n", + "frag_star = estrella1[12:15,0:70]\n", + "print(frag_star.shape)\n", + "plt.imshow(frag_star, cmap=\"gray\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como ahora **frag_star** representa una matriz de (3,70) no es suficiente solo hacer un arreglo lineal de las variables x e y (como en el caso anteior). Por lo tanto, Y será un arreglo de 210 valores (producto de filas por columnas) y X fue diseñado como un arreglo de 3 matrices de 70 elementos con el fin de obtener 210 elementos para comparar con Y.\n", + "\n", + "Graficamos xf vs Y y obtenemos todos los valores posibles contenidos en la banda de imagen." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fc82e30f400>]" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y = frag_star.reshape(210)\n", + "x = np.arange(70) \n", + "xf = np.array([x,x,x]).reshape(210)\n", + "\n", + "\n", + "plt.plot(xf,y,\".\")" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "#Definimos la función modelo:\n", + "def gauss1D(params, x):\n", + " exponente = -((x-params[1])**2)/(2*(params[2])**2)\n", + " \n", + " ymodel = params[0]*np.exp(exponente) + params[3]\n", + " return ymodel\n", + "\n", + "#Params[0]: Constante de amplitud\n", + "#Params[1]: Xo\n", + "#Params[2]: desv. std\n", + "#Params[3]: constante aditiva" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[145.52246647 32.43940401 4.98444533 117.26091299]\n" + ] + } + ], + "source": [ + "#Definimos la función error:\n", + "def Error(tpl,x,y):\n", + " ymodel = gauss1D(tpl,x)\n", + " errors = (ymodel-y)\n", + " return errors\n", + "\n", + "params1D =[140,32,4,120]\n", + "p0 = params1D\n", + "best,suss = leastsq(Error, p0, args=(xf,y))\n", + "print(best)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fc82dfd9208>]" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ymodel_opt = gauss1D(best,xf)\n", + "plt.title(\"Gaussiana optimizada de la banda de la estrella 1\")\n", + "plt.plot(xf,ymodel_opt,'r.')\n", + "plt.plot(xf,y,\"b.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Para una banda de la segunda estrella:**" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 30)\n" + ] + }, + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82de17a90>" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Recortando las filas medias de la estrella 2\n", + "frag_star2 = estrella2[16:19,0:30]\n", + "print(frag_star2.shape)\n", + "plt.imshow(frag_star2, cmap=\"gray\")" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fc82ddf19b0>]" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y2 = frag_star2.reshape(90)\n", + "x2 = np.arange(30)\n", + "xf2 = np.array([x2,x2,x2]).reshape(90)\n", + "\n", + "plt.plot(xf2,y2,\".\")" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[128.16071095 20.38057866 2.04737263 109.61442948]\n" + ] + } + ], + "source": [ + "params1D_2 = [145,20,2.7,105]\n", + "p1 = params1D_2\n", + "best2,suss = leastsq(Error, p1, args=(xf2,y2))\n", + "print(best2)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fc82dd10780>]" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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IF9ge/drtZuZmNiMsm5l9y8yGzOxlM7ukFo0WSZLU5udY9G248EVY9O2gLB8UzBs/nWDe+OmaNz6iKBdZvwt8G9hSWGlm7cCVwL8WVF8DLA5/VgAPhY8iUkT2thUMLR8iNxWyn4LzX1pBqt6NajCaN358yvbg3f1Z4O0xXvp7YAPnsocAq4AtHtgFpM1sVlVaKpJQmTVLyU0zmAK5aUZmzdJ6N0kSYlzDJM1sFXDE3QfNrPClOcChgvLhsO7YGPtYC6wFmDdv3niaIZII6XQXLa3nBUMAW7Vs3Vg0THJ8Kg7wZvZvgbsI0jPj5u6bgE0AnZ2dXmZzkcRS+qG8sRbn1u+pvPH04BcCC4B8730u8KKZXQocAdoLtp0b1olICVq2rrT84tz5Hrz+yomm4gDv7q8AH8uXzewA0Onub5nZ48Cfm9k2gourWXf/UHpGRKQSqdRKOv5vN5ljT5KedQ2py3UyjKJsgDezR4AuYIaZHQbudveHi2z+BHAtMAS8D3ypSu0UkWbW00Nq49ZwdNFWyMyB3t46N6rxmXv909+dnZ0+MDBQ72aISKNavBiGhs6VFy2CN9+sX3sahJm94O6dxV7XnawiNaIpgKuou7t0eRT97gOaTVKkBjSsr8ry6Zi+viC4l0jP6Hd/jnrwIjWg2Q9roLc3SMuUyb2PNaSyWSnAi9SAZj+sn/yQymDemuYeUqkUjUgNvPvuSyXLUju6cewcBXgRSRzdOBZQikakBmbOvBmz6YBhNp2ZM2+ud5OaikbRBNSDF6mBVGoly5btUJqgDjSK5hwFeJEaUZqgPjQx2TlK0YhIomgUzTnqwYtIomgUzTkK8CKSOEqPBZSiERFJKAV4EZGEUoAXEUkoBXgRSRzd6BRQgBep0NGjmxgcvIqjRzeV3rCnJ1iooqdnchomwLkbnfbv/yqDg1c0dZDXKBqRChw9uok33vgyAO+88xQAs2ev/fCGPT2wcWPwPP+oJeYmhW50OqdsD97MNpvZCTN7taDub83s52b2spn90MzSBa/daWZDZva6mV1Vo3aL1MXw8PaS5bP6+kqXpWZ0o9M5UVI03wWuHlX3NPBJd/8U8AZwJ4CZLQFWA0vD9/yDmU2pWmtF6uyCC5aVLJ9V4RJzUj35G50WLPh6U89DAxFSNO7+rJnNH1X3VEFxF/BH4fNVwDZ3PwnsN7Mh4FKgeZNgkiitrWnAAAcsLI+hgiXmpPpS9z5Gqq8PujPQWzzAZ7P9ib7jtRo5+NuAH4TP5xAE/LzDYd2HmNlaYC3AvHnzqtAMkfGL+kUP/vw/7+xMhSX//O/tVWCvh4jXP5ph1skJjaIxs78CRoCtlb7X3Te5e6e7d7a1tU2kGSITUsmoC/35HwMRr380w7q54w7wZnYrcB1wk7t7WH0EaC/YbG5YJ9KwKl2kOZVaycUX36ng3qgiXv9ohnVzx5WiMbOrgQ3AH7j7+wUvPQ5838weAGYDi4HnJ9xKkRpKp7swa8U9h1lrU4+6SISI1z9On/4VQR83B7SE5eLimK8vG+DN7BGgC5hhZoeBuwlGzUwHnjYzgF3u/ifu/pqZPQrsIUjdrHP3M7VqvEj1+KhHibUI1z+CE/tU3E9hNrXkiT2b7Wf37s+G205j2bIdsQjyUUbRfH6M6odLbH8PcM9EGiVSLVF6XZnMToJ+iON+pqlvjGk+0U7sx49vIRgcCO4nOX58Syz+j+hOVkmsqKMk8jfGRBoZI4nRDCd2zUUjiRV1lEQqtZJFi77JhRdewaJF3yz/JdccM4lQyR2vH/nI8pLlQpHnKpoE6sFLYkUdJZHN9jM0tJ5c7hTZ7E85//zfKR7kNcdMYlSytF/UC7KR5yqaJOrBS2Kd+1JCqS9lRcMkNcdMoqTufYyL/8NmUvc+VnK7oLc/naC3P71ob//YsYdLliebArwkVtQvZUWTU2mOmeTI/zU2NBQ8lki5Rb3Bbdq02SXLk83O3aNUP52dnT4wMFDvZkgCHT26ieHh7bS13VDyT+XsfV8gc+xJ0rOuIXXn/yi9054ezTGTBIsXB8E9b9EiePPNCe0ym+3npZcuJxgl3sry5c+WTP1E/f9ZjJm94O6dxV5XDn6USm5miOOND80km+1naO86coyQfevHxXPrPT2kNm4lBcBWyMwpHbg1x0wydHefu46SL5cS4cSeSq1k+fJnI8WFycjXxztFU8lohgjbZrP9DA5czv6huxgcuLzknCSVbFvtdjb7PrP3fYGD/+WjZO/7QsntMtvuIMcITIGcj5DZdsfYGyqv3px6e2HDhqDnvmFD6ZN2JemciHn9Y8ce/sAw/Jrk69297j+f/vSnvWIbNrjDuZ8NGya87YHvXO47/hnfsQPf8TR+4DuXF91l5G03bPDMEvzAGjyzpDrtrMWxV7rPSMdUg3Zm7r3Jdz6F7/gxvvMpPHPvTUV3mblyjv/kyeDz+cmTeObKORNvpzSnRYs++H9k0aKxt6vg/9IrjyzyHT8OY8iP8VceKbLPEoABLxFb49uDr6TXFXHbdN8+bAQ4A3YmKBcTddvs7q0MfgP23waD3wjKE21nLY6dvj6yS+DgGsguKb3PyMdUwT6jtvPQ9B/irYCBtwblYlLLbqLjdljwT9Bxe1AeUyU9OWlOUS+uV/DdbP+n/xfEEAcbCcrVFt8AX8lohqjbdnUFaznkf7q6iu8z4raZ7oXkphKkCVqD8oTbWYNjz9624oNB+7YVRXcZ9Zgq2Sfd3R88GRRp58kFv1Wy/AG9vaSu28DFzy8idV2ZwN3bG1xgU3CXsUTtBFTw3Uwtu4ll62HBf4dl60t0QCYgvhdZK1kxJ+K2mTVL8X0GLY63Gpk1S8MLb+PfNr36fmzg9/HcGcymkF59/8SPqUbHnvsFYJBrKX3sUY8ps2YpufB3lLPS+8zedT2DVz5AjhFaaKWj8/oxt5214mu8+/qXz+YuZ634WrEjD+iCqFRLlP9LFX43UxCuPFWbEVnxDfBQ2Zc34uxyLa3haj2tpcdDV7Itra3gObDyv+7sXdeT+bN0cAW+CtsB7FsLw38IbW1Q7O+HqVM/GvwlAmBefm7s1inhMRVfcreS31Ems5PclOCu0xy5ovOC5EcZTGRomUhNVTkuTUSsA/y+fT0MD/fR1tbNwoUT/yXl5yTJB49SQ5yi3uYcTGgUJNrcR0pOaBRMSdqF+2nMprJs2djbVrLU2L59PRw6FAwFyz+O9bt6992XSpY/fEzlJ2mq5PdZyeILs2evVWAXiSC2Ofh84PrNb4Y4dGgj+/aVHlq3b18Pu3YtLrldfk6Sd955hqGh9aWHPhJtZZ9K7pIMpiQ9RRA4T3H8+JYxtwturf8NUZYaGx7uK1kej/wCGWAlF8io5PdZyQlGRKKJbYCvJHBFPRlUunRbNtvPwYP3lV3DM+pMhadPHy9ZzhsZyXBuAG0uLI+tra27ZDlv5sybMZtGELSnMXPmzUX3GSg/j3YzrHkp0shiG+CjBi6IfjKopLcddaHmSnqxU6fOLFnOe++93SXLhRYu7KW9fQPnnbeI9vYNRVNZqdRKli3byYIF9xRNDeWNlaIZSyVpl+AEM53gBDM9wglGRMqJbQ4+H6ii5ODb2rrP5p/z5bFUMn3oWL39sbaPuh0EQe748c1nc/DFglxb2w1nb23Ol0tZuLA30jWKVGplpCkXoi6QUcmal8EJZoemfhCpotgGeIgeuCo5GVQ7yFWyWlC+F10uyNV7JEnUE2F+NseoKyVF/d2LSDRlZ5M0s83AdcAJd/9kWHcR8ANgPnAAuNHd37FgBe4HgWuB94Fb3f3Fco2I62ySUScba+ZJyZr52EVqrdxsklEC/OXAe8CWggC/EXjb3e83szuAC929x8yuBf6CIMCvAB509xK3LwbiGuBFROqpXIAve5HV3Z8F3h5VvQr4Xvj8e8D1BfVbwnlwdgFpM5tVcatFRGTCxjuK5uPufix8fhz4ePh8DnCoYLvDYd2HmNlaMxsws4Hh4eFxNkNERIqZ8DDJcMrKipeFcvdN7t7p7p1tbW0TbYaIiIwy3gD/y3zqJXw8EdYfAdoLtpsb1omIyCQbb4B/HLglfH4L8KOC+pstcBmQLUjliIjIJCo7Dt7MHgG6gBlmdhi4G7gfeNTM/hg4CNwYbv4EwQiaIYJhkl+qQZtFRCSCssMkJ6URZsMEJ4rxmAG8VcXmNIKkHVPSjgeSd0xJOx5I3jGNdTwXu3vRi5gNEeAnwswGSo0DjaOkHVPSjgeSd0xJOx5I3jGN53hiO9mYiIiUpgAvIpJQSQjwm+rdgBpI2jEl7XggeceUtOOB5B1TxccT+xy8iIiMLQk9eBERGYMCvIhIQsU6wJvZ1Wb2upkNhdMWx56ZHTCzV8xst5nFbg5lM9tsZifM7NWCuovM7GkzezN8vLCebaxUkWP6GzM7En5Ou8OpsmPBzNrNbIeZ7TGz18zsK2F9LD+nEscT58/oPDN73swGw2P6Wli/wMyeC2PeDyxYSLn4fuKagzezKcAbwH8kmLXyX4DPu/ueujZsgszsANDp7rG8QaOS9QPq2c5KFDmmvwHec/e/q2fbxiOcP2qWu79oZh8BXiCY8vtWYvg5lTieG4nvZ2TA+e7+nplNBX4GfAX4r0Cfu28zs+8Ag+7+ULH9xLkHfykw5O6/cPdTwDaC+eiljipcPyAWihxTbLn7sfxKa+7+LrCXYFrvWH5OJY4ntsI1Nd4Li1PDHwc+B/yvsL7sZxTnAB957vmYceApM3vBzCZ3sdXaKbZ+QNz9uZm9HKZwYpHOGM3M5gPLgedIwOc06nggxp+RmU0xs90Es/U+DewDMu4+Em5SNubFOcAn1e+5+yXANcC6MD2QGONdP6ABPQQsBJYBx4Bv1LU142BmFwDbgfXu/uvC1+L4OY1xPLH+jNz9jLsvI5h2/VLg31W6jzgH+ETOPe/uR8LHE8APCT7YuCu2fkBsufsvwy9gDvhHYvY5hXnd7cBWd+8Lq2P7OY11PHH/jPLcPQPsAFYSLIOanwW4bMyLc4D/F2BxeFV5GrCaYD762DKz88OLRJjZ+cCVwKul3xULxdYPiK1Raw3/ITH6nMILeA8De939gYKXYvk5FTuemH9GbWaWDp//G4LBJHsJAv0fhZuV/YxiO4oGIBz29E1gCrDZ3e+pb4smxsx+m6DXDsFc/d+P2zEVrh8A/JJg/YDHgEeBeYTrB7h7bC5aFjmmLoI//R04AHw5LovbmNnvAT8FXgFyYfVdBHnr2H1OJY7n88T3M/oUwUXUKQQd8Ufd/b+FMWIbcBHwEvAFdz9ZdD9xDvAiIlJcnFM0IiJSggK8iEhCKcCLiCSUAryISEIpwIuIJJQCvIhIQinAi4gk1P8HN0E6C3/tzH8AAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ymodel_opt_2 = gauss1D(best2,xf2)\n", + "plt.plot(xf2,ymodel_opt_2,'r.')\n", + "plt.plot(xf2,y2,\"y.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Para una banda de la tercera estrella:**" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 20)\n" + ] + }, + { + "data": { + "text/plain": [ + "<matplotlib.image.AxesImage at 0x7fc82dcb13c8>" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Obteniendo las filas de la estrella 3\n", + "frag_star3 = estrella3[13:16,0:20]\n", + "print(frag_star3.shape)\n", + "plt.imshow(frag_star3, cmap=\"gray\")" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fc82dc0ddd8>]" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y3 = frag_star3.reshape(60)\n", + "x3 = np.arange(20)\n", + "xf3 = np.array([x3,x3,x3]).reshape(60)\n", + "\n", + "plt.plot(xf3,y3,\".\")" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[120.4001433 7.87976037 1.36833569 118.42969041]\n" + ] + } + ], + "source": [ + "params1D_3 = [140,8.13,1.5,115]\n", + "p2 = params1D_3\n", + "best3,suss = leastsq(Error, p2, args=(xf3,y3))\n", + "print(best3)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fc82dbd2128>]" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Grafico de nuestra gaussiana optimizada(verde) y nuestros valores reales(cyan).\n", + "ymodel_opt_3 = gauss1D(best3,xf3)\n", + "plt.plot(xf3,ymodel_opt_3,'g.')\n", + "plt.plot(xf3,y3,\"c.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}