diff --git a/starlight.ipynb b/starlight.ipynb index d42566556c90ceaee81d0c79aec66fff13ea77f2..f1e6987f60e4004f19433398634e16d8353db1ed 100644 --- a/starlight.ipynb +++ b/starlight.ipynb @@ -216,8 +216,7 @@ "ax2[1].title.set_text(\"Estrella ejemplo stars[2]\")\n", "\n", "ax2[0].imshow(portion, \"Greys_r\")\n", - "ax2[1].imshow(stars[2], \"Greys_r\")\n", - "\n" + "ax2[1].imshow(stars[2], \"Greys_r\")" ] }, { @@ -225,27 +224,26 @@ "metadata": {}, "source": [ "Ahora definimos la función Gaussiana que se busca ajustar, y la función que calcula los errores. La primera, `gauss`, toma de argumentos coordenadas $x$ y $y$, además de una tupla de `params` organizada de esta manera:\n", - "* ($x_0$, $y_0$, $s$, $c$)\n", + "* ($x_0$, $y_0$, $s$, $c$, $A$)\n", "\n", "y retorna\n", "$$\n", - "gauss = e^{-(x^2+y^2)/2s^2}+c.\n", + "A\\cdot gauss = e^{-(x^2+y^2)/2s^2}+c.\n", "$$\n", - "Notemos que la amplitud es siempre igual a 1. Esto se hace con la intención de normalizar los datos de la estrella, para reducir el número de parámetros de ajuste y facilitar la comparación de datos.\n", "\n", "La función `error` es sencilla, solo vale la pena mencionar que se debe reducir la dimensión del arreglo de errores con la función `flatten()`." ] }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ "def gauss(params,x, y):\n", " x0,y0=params[1],params[0]\n", " dis=(x-x0)**2+(y-y0)**2\n", - " return np.exp(-0.5*dis**2/params[2]**2)+params[3]\n", + " return params[4]*np.exp(-0.5*dis**2/params[2]**2)+params[3]\n", "\n", "def error(tpl, x,y, mat):\n", " er = gauss(tpl, x,y)-mat\n", @@ -263,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 177, "metadata": {}, "outputs": [], "source": [ @@ -272,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 178, "metadata": {}, "outputs": [], "source": [ @@ -286,11 +284,11 @@ " \n", " inix,iniy = np.where(item == np.amax(item)) # Calculamos el centro de la imagen \n", " # para usar las coordenadas como centro\n", - " p0 = (inix[0], iniy[0], 5.,0.1) # de la Gaussiana, y llenamos el vector \n", + " p0 = (inix[0], iniy[0], 5.,0.1, np.amax(item)) # de la Gaussiana, y llenamos el vector \n", " # de condiciones iniciales\n", " \n", - " best = least_squares(error, p0, args = (X,Y, item/np.amax(item))) # Le pasamos al optimizador los errores\n", - " # comparando con los datos normalizados.\n", + " best = least_squares(error, p0, args = (X,Y, item)) # Le pasamos al optimizador los errores\n", + " # comparando con los datos normalizados.\n", " results.append(best)" ] }, @@ -298,17 +296,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Mostramos los resultados de la estrella `star[2]` como ejemplo: los contornos representan la función de mejor ajuste. Para comparar aplicamos a la función la inversa del parámetro de normalización." + "Mostramos los resultados de la estrella `star[2]` como ejemplo: los contornos representan la función de mejor ajuste." ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 179, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 576x432 with 3 Axes>" ] @@ -328,102 +326,157 @@ "fig3, ax3= subplots(figsize=(8,6))\n", "\n", "im1 = ax3.imshow(stars[2], \"Greys_r\")\n", - "im2 = ax3.contour(X,Y, gauss(results[2].x, X,Y)*np.amax(stars[2]), cmap=\"summer_r\")\n", + "im2 = ax3.contour(X,Y, gauss(results[2].x, X,Y), cmap=\"summer_r\")\n", "\n", "cbar_im1 = colorbar(im1)\n", "cbar_im2 = colorbar(im2)" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Finalmente, convertimos los datos del ajuste para obtener el FWHM (**F**ull **W**idth at **H**alf **M**aximum), sabiendo que para la Gaussiana\n", + "$$\n", + "FWHM = 2\\sqrt{2s\\ln 2}.\n", + "$$\n", + "\n", + "Con estos datos realizamos un histograma y calculamos la media, mediana, y desviación estándar." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 180, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "fwhm = np.array([item.x[2] for item in results])\n", + "\n", + "fwhm = 2*np.sqrt(2*fwhm*np.log(2))" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 181, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 648x432 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig4, ax4=subplots(figsize=(9,6))\n", + "ylabel(\"Frecuencia\", fontsize=20)\n", + "xlabel(\"FWHM\", fontsize=20)\n", + "n1, bins1, patches1=hist(fwhm, 10, alpha=0.6)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 191, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "El promedio es 5.932\n", + "La mediana es 3.335\n", + "La desviación estándar es 5.851\n" + ] + } + ], + "source": [ + "avg = np.mean(fwhm)\n", + "med = np.median(fwhm)\n", + "dev = np.std(fwhm)\n", + "\n", + "print(\"El promedio es \"+ '{:.3f}'.format(avg)+\"\\nLa mediana es \"+ \"{:.3f}\".format(med)\n", + " + \"\\nLa desviación estándar es \"+ \"{:.3f}\".format(dev))" + ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### Parte final\n", + "Realizamos todo de nuevo pero usando el canal azul, ya que es el que menos peso tiene en la conversión a escala de grises. En este caso obviamos la mayorÃa de figuras, y mostramos directamente los resultados estadÃsticos. Observamos en general Gaussianas más anchas, y una acentuación de los extremos atÃpicos." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 193, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "El promedio es 10.202\n", + "La mediana es 4.170\n", + "La desviación estándar es 15.044\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 648x432 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "blueSky = sky[:,:,2]\n", + "\n", + "portion = blueSky[280:420,400:650]\n", + "stars=[portion[85:105,40:60],portion[20:30,73:85],\n", + " portion[28:45,220:235],portion[114:123,218:226],\n", + " portion[55:65,135:145],portion[70:80,135:145],\n", + " portion[80:89,148:156],portion[42:50,161:170]]\n", + "\n", + "results = []\n", + "\n", + "for item in stars: \n", + " Y=np.arange(len(item[:,0]))\n", + " X=np.arange(len(item[0,:])) \n", + " \n", + " X,Y=np.meshgrid(X,Y) \n", + " \n", + " inix,iniy = np.where(item == np.amax(item)) \n", + " p0 = (inix[0], iniy[0], 5.,0.1, np.amax(item)) \n", + " \n", + " best = least_squares(error, p0, args = (X,Y, item)) \n", + " results.append(best)\n", + "\n", + "fwhm = np.array([item.x[2] for item in results])\n", + "fwhm = 2*np.sqrt(2*fwhm*np.log(2))\n", + "\n", + "fig5, ax5=subplots(figsize=(9,6))\n", + "ylabel(\"Frecuencia\", fontsize=20)\n", + "xlabel(\"FWHM (Solo azul)\", fontsize=20)\n", + "n1, bins1, patches1=hist(fwhm, 10, alpha=0.6)\n", + "\n", + "avg = np.mean(fwhm)\n", + "med = np.median(fwhm)\n", + "dev = np.std(fwhm)\n", + "\n", + "print(\"El promedio es \"+ '{:.3f}'.format(avg)+\"\\nLa mediana es \"+ \"{:.3f}\".format(med)\n", + " + \"\\nLa desviación estándar es \"+ \"{:.3f}\".format(dev))" + ] } ], "metadata": {