diff --git a/Clase_estrella.py b/Clase_estrella.py
index c5aaf43e9c99db9e0a6b4ca249d5dd8317e426bd..6016a4aa579d38d2de124afbd66b7f217c0fb3c3 100644
--- a/Clase_estrella.py
+++ b/Clase_estrella.py
@@ -17,7 +17,7 @@ import statistics as stat
class Estrella_a:
def __init__(self, estrella_map):
- print('Convertido a estrella')
+ #print('Convertido a estrella')
self.estrella_map = estrella_map
def x_y_grilla(self):
@@ -30,8 +30,11 @@ class Estrella_a:
paso_x = 255/nx
paso_y = 255/ny
- x = np.arange(0,255,paso_x)
- y = np.arange(0,255,paso_y)
+ #x = np.arange(0,1,paso_x)
+ #y = np.arange(0,1,paso_y)
+
+ x = np.arange(0,nx,1)
+ y = np.arange(0,ny,1)
#Grilla
yy, xx = np.meshgrid(x,y)
@@ -175,81 +178,5 @@ class Estrella_a:
#img=plt.imshow(data_graf,cmap="gray")
- return data_graf #img, data_graf
-
- def estadistica(self, p):
-
- """
- Retorna en su orden la moda, media, mediana, desviacion del ajuste realizado
-
- --------------------------------------
-
- FUNCIONAMIENTO:
-
- Ingrese la función como:
-
- estadistica(p)
-
- PARÃMETROS:
-
- p: Tipo(array 1D) -> Parámetros de ajuste. Debe contener los siguientes parámetros:
-
- p = [a, b, c, x0, y0]
-
- a: Amplitud de la Gaussiana
- b: Offset
- c: Varianza
- x0: Centro en x
- y0: Centro en y
-
- """
-
- imagen_es = self.ajusteGauss(p)
-
- moda=stat.mode(np.round(imagen_es,3).reshape(-1))
-
- media=np.mean(imagen_es)
-
- mediana=np.median(imagen_es)
-
- desviacion=imagen_es.std()
-
- return moda, media, mediana, desviacion
-
- def histograma(self,p, conjuntos):
+ return data_graf, ajuste #img, data_graf
- """
- Retorna el histograma del ajuste realizado
-
- --------------------------------------
-
- FUNCIONAMIENTO:
-
- Ingrese la función como:
-
- histograma(p)
-
- PARÃMETROS:
-
- p: Tipo(array 1D) -> Parámetros de ajuste. Debe contener los siguientes parámetros:
-
- p = [a, b, c, x0, y0]
-
- a: Amplitud de la Gaussiana
- b: Offset
- c: Varianza
- x0: Centro en x
- y0: Centro en y
-
- conjuntos: Tipo(int) -> Cantidad de bins para la función de plt.hist
-
- """
-
- imagen_es = self.ajusteGauss(p)
-
- plt.figure(figsize=(9,4))
- plt.hist(imagen_es,bins=conjuntos, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)
- plt.show()
-
-
-
diff --git a/Entrega.ipynb b/Entrega.ipynb
index d4411785f87b3b5f201609abdde23b177047e3cc..4ee2a3fbc11f71e2a0bb7160d02e32a00ec0d08f 100644
--- a/Entrega.ipynb
+++ b/Entrega.ipynb
@@ -47,7 +47,8 @@
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from scipy.optimize import leastsq # Para optimizar las funciones\n",
- "import statistics as stat"
+ "import statistics as stat\n",
+ "import math"
]
},
{
@@ -112,7 +113,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7fd7bbf0ed68>"
+ "<matplotlib.image.AxesImage at 0x7fcc33b1e7b8>"
]
},
"execution_count": 4,
@@ -311,7 +312,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7fd7bbe70c18>"
+ "<matplotlib.image.AxesImage at 0x7fcc33a172b0>"
]
},
"execution_count": 8,
@@ -628,93 +629,6 @@
"ax2.set_title('Ajuste')"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Análisis estadistico**"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mediana: 121.00032292471543\n",
- "Media: 136.12938530499758\n",
- "Moda: 119.161\n",
- "Desviacion estandar 1: 31.37796037027831\n",
- "Desviacion estandar 2: 31.36515042470658\n"
- ]
- }
- ],
- "source": [
- "#Media\n",
- "media=np.mean(zz)\n",
- "\n",
- "#Mediana\n",
- "mediana=np.median(zz)\n",
- "\n",
- "#Moda\n",
- "moda=stat.mode(np.round(zz,3).reshape(-1))\n",
- "\n",
- "#Desviacion estandar con scipy\n",
- "desviacion=stat.stdev(zz.reshape(-1))\n",
- "\n",
- "#Desviacion estandar con numpy\n",
- "desviacion1=zz.std()\n",
- "\n",
- "print('Mediana: ', mediana)\n",
- "print('Media: ', media)\n",
- "print('Moda: ', moda)\n",
- "print('Desviacion estandar 1: ', desviacion)\n",
- "print('Desviacion estandar 2: ', desviacion1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Para hacer el histograma"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 648x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(9,4))\n",
- "plt.hist(zz, bins=5, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)\n",
- "plt.show()"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -733,7 +647,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -773,6 +687,8 @@
"\n",
" star = estrellita_[z_[0,fila]-len(ancho)-5:z_[0,fila]+len(ancho)+5, z_[1,fila]-len(ancho)-5:z_[1,fila]+len(ancho)+5]\n",
" \n",
+ " #star = estrellita_[z_[0,fila]-10:z_[0,fila]+10, z_[1,fila]-10:z_[1,fila]+10]\n",
+ " \n",
" #Retorna el array correspondiente a la imagen del miniframe\n",
" \n",
" return radio, star\n"
@@ -780,7 +696,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -829,7 +745,7 @@
" while k in range(nz-2):\n",
" \n",
" # 2. Es decir que vuelve aquÃ*\n",
- "\n",
+ " \n",
" k += 1\n",
"\n",
" r = abs(z[:,k]-z[:,k+1])\n",
@@ -839,18 +755,27 @@
" if r_sum <= 2:\n",
"\n",
" radio, miniestrella = miniframe_estrella(estrellita, k, z)\n",
- "\n",
+ " \n",
" lista_estrellas.append(miniestrella)\n",
"\n",
" else:\n",
+ " \n",
" continue # 1. Continua con el ciclo*\n",
" \n",
- " return np.array(lista_estrellas)\n"
+ " #lista_estrellas = np.array(lista_estrellas)\n",
+ " return lista_estrellas\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Utilizando la imagen de 'Estrella 2' para el análisis, se encuentran las siguientes entrellas dentro."
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -870,15 +795,16 @@
"lista_estrellas=estrellas_recortadas(estrella_2, 240)\n",
"\n",
"\n",
+ "\n",
"fig, axs = plt.subplots(2, 2, figsize=(6,6))\n",
"\n",
- "axs[0, 0].imshow(lista_estrellas[0,:,:], cmap='gray')\n",
+ "axs[0, 0].imshow(lista_estrellas[0], cmap='gray')\n",
"axs[0, 0].set_title('Estrella 1')\n",
- "axs[0, 1].imshow(lista_estrellas[1,:,:], cmap='gray')\n",
+ "axs[0, 1].imshow(lista_estrellas[1], cmap='gray')\n",
"axs[0, 1].set_title('Estrella 2')\n",
- "axs[1, 0].imshow(lista_estrellas[2,:,:], cmap='gray')\n",
+ "axs[1, 0].imshow(lista_estrellas[2], cmap='gray')\n",
"axs[1, 0].set_title('Estrella 3')\n",
- "axs[1, 1].imshow(lista_estrellas[3,:,:], cmap='gray')\n",
+ "axs[1, 1].imshow(lista_estrellas[3], cmap='gray')\n",
"axs[1, 1].set_title('Estrella 4')\n",
"\n",
"# Hide x labels and tick labels for top plots and y ticks for right plots.\n",
@@ -886,177 +812,239 @@
" ax.label_outer()\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Para el análisis de las anteriores estrellas se hace uso de la clase, se presenta además el ajuste gaussiano de dichas estrellas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Conversion a la clase estrella\n",
+ "\n",
+ "clase_estrella=[]\n",
+ "\n",
+ "for item in lista_estrellas:\n",
+ " clase_estrella.append(Estrella_a(item))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x432 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Impresión de las imagenes\n",
+ "\n",
+ "#Para recordar: p = [a, b, c, x0, y0]\n",
+ "\n",
+ "\n",
+ "p1=np.array([1,0,1,5,5])\n",
+ "\n",
+ "graficas=[]\n",
+ "\n",
+ "parametros=[]\n",
+ "\n",
+ "\n",
+ "for i in range(0,len(clase_estrella)):\n",
+ " uno, dos = clase_estrella[i].ajusteGauss(p1)\n",
+ " \n",
+ " graficas.append(uno)\n",
+ " \n",
+ " parametros.append(dos)\n",
+ "\n",
+ "\n",
+ "#Impresion de imagenes\n",
+ "\n",
+ "fig, axs = plt.subplots(2, 2, figsize=(6,6))\n",
+ "\n",
+ "axs[0, 0].imshow(graficas[0], cmap='gray')\n",
+ "axs[0, 0].set_title('Estrella 1')\n",
+ "axs[0, 1].imshow(graficas[1], cmap='gray')\n",
+ "axs[0, 1].set_title('Estrella 2')\n",
+ "axs[1, 0].imshow(graficas[2], cmap='gray')\n",
+ "axs[1, 0].set_title('Estrella 3')\n",
+ "axs[1, 1].imshow(graficas[3], cmap='gray')\n",
+ "axs[1, 1].set_title('Estrella 4')\n",
+ "\n",
+ "# Hide x labels and tick labels for top plots and y ticks for right plots.\n",
+ "for ax in axs.flat:\n",
+ " ax.label_outer()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Análisis estadistico**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluar=np.array(parametros)\n",
+ "\n",
+ "\n",
+ "FWHM=evaluar*2*math.sqrt(2*math.log(2))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "zz=FWHM"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mediana: 2.9331335083105\n",
+ "Media: 6.958263965191911\n",
+ "Moda: 15.497\n",
+ "Desviacion estandar 1: 6.4437838006164565\n",
+ "Desviacion estandar 2: 6.280623550702009\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Media\n",
+ "media=np.mean(zz)\n",
+ "\n",
+ "#Mediana\n",
+ "mediana=np.median(zz)\n",
+ "\n",
+ "#Moda\n",
+ "moda=stat.mode(np.round(zz,3).reshape(-1))\n",
+ "\n",
+ "#Desviacion estandar con scipy\n",
+ "desviacion=stat.stdev(zz.reshape(-1))\n",
+ "\n",
+ "#Desviacion estandar con numpy\n",
+ "desviacion1=zz.std()\n",
+ "\n",
+ "print('Mediana: ', mediana)\n",
+ "print('Media: ', media)\n",
+ "print('Moda: ', moda)\n",
+ "print('Desviacion estandar 1: ', desviacion)\n",
+ "print('Desviacion estandar 2: ', desviacion1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Para hacer el histograma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 648x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Histograma\n",
+ "\n",
+ "plt.figure(figsize=(9,4))\n",
+ "plt.hist(zz, bins=5, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "------------------------------------------------------------------"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "estrellas_para_estadistica = estrellas_recortadas(imagen_grisss, 230)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[NbConvertApp] WARNING | pattern 'ENTREGA.ipynb' matched no files\r\n",
- "This application is used to convert notebook files (*.ipynb) to various other\r\n",
- "formats.\r\n",
- "\r\n",
- "WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.\r\n",
- "\r\n",
- "Options\r\n",
- "=======\r\n",
- "The options below are convenience aliases to configurable class-options,\r\n",
- "as listed in the \"Equivalent to\" description-line of the aliases.\r\n",
- "To see all configurable class-options for some <cmd>, use:\r\n",
- " <cmd> --help-all\r\n",
- "\r\n",
- "--debug\r\n",
- " set log level to logging.DEBUG (maximize logging output)\r\n",
- " Equivalent to: [--Application.log_level=10]\r\n",
- "--generate-config\r\n",
- " generate default config file\r\n",
- " Equivalent to: [--JupyterApp.generate_config=True]\r\n",
- "-y\r\n",
- " Answer yes to any questions instead of prompting.\r\n",
- " Equivalent to: [--JupyterApp.answer_yes=True]\r\n",
- "--execute\r\n",
- " Execute the notebook prior to export.\r\n",
- " Equivalent to: [--ExecutePreprocessor.enabled=True]\r\n",
- "--allow-errors\r\n",
- " Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.\r\n",
- " Equivalent to: [--ExecutePreprocessor.allow_errors=True]\r\n",
- "--stdin\r\n",
- " read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'\r\n",
- " Equivalent to: [--NbConvertApp.from_stdin=True]\r\n",
- "--stdout\r\n",
- " Write notebook output to stdout instead of files.\r\n",
- " Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]\r\n",
- "--inplace\r\n",
- " Run nbconvert in place, overwriting the existing notebook (only \r\n",
- " relevant when converting to notebook format)\r\n",
- " Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]\r\n",
- "--clear-output\r\n",
- " Clear output of current file and save in place, \r\n",
- " overwriting the existing notebook.\r\n",
- " Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]\r\n",
- "--no-prompt\r\n",
- " Exclude input and output prompts from converted document.\r\n",
- " Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]\r\n",
- "--no-input\r\n",
- " Exclude input cells and output prompts from converted document. \r\n",
- " This mode is ideal for generating code-free reports.\r\n",
- " Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True]\r\n",
- "--allow-chromium-download\r\n",
- " Whether to allow downloading chromium if no suitable version is found on the system.\r\n",
- " Equivalent to: [--WebPDFExporter.allow_chromium_download=True]\r\n",
- "--log-level=<Enum>\r\n",
- " Set the log level by value or name.\r\n",
- " Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']\r\n",
- " Default: 30\r\n",
- " Equivalent to: [--Application.log_level]\r\n",
- "--config=<Unicode>\r\n",
- " Full path of a config file.\r\n",
- " Default: ''\r\n",
- " Equivalent to: [--JupyterApp.config_file]\r\n",
- "--to=<Unicode>\r\n",
- " The export format to be used, either one of the built-in formats\r\n",
- " ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf',\r\n",
- " 'python', 'rst', 'script', 'slides', 'webpdf'] or a dotted object name that\r\n",
- " represents the import path for an `Exporter` class\r\n",
- " Default: ''\r\n",
- " Equivalent to: [--NbConvertApp.export_format]\r\n",
- "--template=<Unicode>\r\n",
- " Name of the template to use\r\n",
- " Default: ''\r\n",
- " Equivalent to: [--TemplateExporter.template_name]\r\n",
- "--template-file=<Unicode>\r\n",
- " Name of the template file to use\r\n",
- " Default: None\r\n",
- " Equivalent to: [--TemplateExporter.template_file]\r\n",
- "--writer=<DottedObjectName>\r\n",
- " Writer class used to write the results of the conversion\r\n",
- " Default: 'FilesWriter'\r\n",
- " Equivalent to: [--NbConvertApp.writer_class]\r\n",
- "--post=<DottedOrNone>\r\n",
- " PostProcessor class used to write the results of the conversion\r\n",
- " Default: ''\r\n",
- " Equivalent to: [--NbConvertApp.postprocessor_class]\r\n",
- "--output=<Unicode>\r\n",
- " overwrite base name use for output files. can only be used when converting\r\n",
- " one notebook at a time.\r\n",
- " Default: ''\r\n",
- " Equivalent to: [--NbConvertApp.output_base]\r\n",
- "--output-dir=<Unicode>\r\n",
- " Directory to write output(s) to. Defaults to output to the directory of each\r\n",
- " notebook. To recover previous default behaviour (outputting to the current\r\n",
- " working directory) use . as the flag value.\r\n",
- " Default: ''\r\n",
- " Equivalent to: [--FilesWriter.build_directory]\r\n",
- "--reveal-prefix=<Unicode>\r\n",
- " The URL prefix for reveal.js (version 3.x). This defaults to the reveal CDN,\r\n",
- " but can be any url pointing to a copy of reveal.js.\r\n",
- " For speaker notes to work, this must be a relative path to a local copy of\r\n",
- " reveal.js: e.g., \"reveal.js\".\r\n",
- " If a relative path is given, it must be a subdirectory of the current\r\n",
- " directory (from which the server is run).\r\n",
- " See the usage documentation\r\n",
- " (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-\r\n",
- " slideshow) for more details.\r\n",
- " Default: ''\r\n",
- " Equivalent to: [--SlidesExporter.reveal_url_prefix]\r\n",
- "--nbformat=<Enum>\r\n",
- " The nbformat version to write. Use this to downgrade notebooks.\r\n",
- " Choices: any of [1, 2, 3, 4]\r\n",
- " Default: 4\r\n",
- " Equivalent to: [--NotebookExporter.nbformat_version]\r\n",
- "\r\n",
- "Examples\r\n",
- "--------\r\n",
- "\r\n",
- " The simplest way to use nbconvert is\r\n",
- " \r\n",
- " > jupyter nbconvert mynotebook.ipynb --to html\r\n",
- " \r\n",
- " Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].\r\n",
- " \r\n",
- " > jupyter nbconvert --to latex mynotebook.ipynb\r\n",
- " \r\n",
- " Both HTML and LaTeX support multiple output templates. LaTeX includes\r\n",
- " 'base', 'article' and 'report'. HTML includes 'basic' and 'full'. You\r\n",
- " can specify the flavor of the format used.\r\n",
- " \r\n",
- " > jupyter nbconvert --to html --template lab mynotebook.ipynb\r\n",
- " \r\n",
- " You can also pipe the output to stdout, rather than a file\r\n",
- " \r\n",
- " > jupyter nbconvert mynotebook.ipynb --stdout\r\n",
- " \r\n",
- " PDF is generated via latex\r\n",
- " \r\n",
- " > jupyter nbconvert mynotebook.ipynb --to pdf\r\n",
- " \r\n",
- " You can get (and serve) a Reveal.js-powered slideshow\r\n",
- " \r\n",
- " > jupyter nbconvert myslides.ipynb --to slides --post serve\r\n",
- " \r\n",
- " Multiple notebooks can be given at the command line in a couple of \r\n",
- " different ways:\r\n",
- " \r\n",
- " > jupyter nbconvert notebook*.ipynb\r\n",
- " > jupyter nbconvert notebook1.ipynb notebook2.ipynb\r\n",
- " \r\n",
- " or you can specify the notebooks list in a config file, containing::\r\n",
- " \r\n",
- " c.NbConvertApp.notebooks = [\"my_notebook.ipynb\"]\r\n",
- " \r\n",
- " > jupyter nbconvert --config mycfg.py\r\n",
- "\r\n",
- "To see all available configurables, use `--help-all`.\r\n",
- "\r\n"
+ "[NbConvertApp] Converting notebook Entrega.ipynb to markdown\n",
+ "[NbConvertApp] Support files will be in Entrega_files/\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Making directory Entrega_files\n",
+ "[NbConvertApp] Writing 14510 bytes to Entrega.md\n"
]
}
],
"source": [
"#Para guardar el notebook a .md\n",
- "! jupyter nbconvert --to markdown ENTREGA.ipynb"
+ "! jupyter nbconvert --to markdown Entrega.ipynb"
]
}
],
diff --git a/Entrega.md b/Entrega.md
index 7cdcb8f0fc461fcb8874fcd96de96b359f6a9fda..b4da81b058fd6b6f809a33f8541a448d7f5afb13 100644
--- a/Entrega.md
+++ b/Entrega.md
@@ -17,6 +17,7 @@ import matplotlib.pyplot as plt
%matplotlib inline
from scipy.optimize import leastsq # Para optimizar las funciones
import statistics as stat
+import math
```
_NOTA:_
@@ -56,7 +57,7 @@ plt.imshow(imagen)
- <matplotlib.image.AxesImage at 0x7fd7bbf0ed68>
+ <matplotlib.image.AxesImage at 0x7f38c3d63940>
@@ -186,7 +187,7 @@ plt.imshow(imagen_grisss, cmap='gray')
- <matplotlib.image.AxesImage at 0x7fd7bbe70c18>
+ <matplotlib.image.AxesImage at 0x7f38c3c5b198>
@@ -363,56 +364,6 @@ ax2.set_title('Ajuste')
-**Análisis estadistico**
-
-Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:
-
-
-```python
-#Media
-media=np.mean(zz)
-
-#Mediana
-mediana=np.median(zz)
-
-#Moda
-moda=stat.mode(np.round(zz,3).reshape(-1))
-
-#Desviacion estandar con scipy
-desviacion=stat.stdev(zz.reshape(-1))
-
-#Desviacion estandar con numpy
-desviacion1=zz.std()
-
-print('Mediana: ', mediana)
-print('Media: ', media)
-print('Moda: ', moda)
-print('Desviacion estandar 1: ', desviacion)
-print('Desviacion estandar 2: ', desviacion1)
-```
-
- Mediana: 121.00032292471543
- Media: 136.12938530499758
- Moda: 119.161
- Desviacion estandar 1: 31.37796037027831
- Desviacion estandar 2: 31.36515042470658
-
-
-Para hacer el histograma
-
-
-```python
-plt.figure(figsize=(9,4))
-plt.hist(zz, bins=5, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)
-plt.show()
-```
-
-
-
-
-
-
-
**EstadÃstica con más estrellas**
Para el análisis de más estrellas se crea una función que busca estrellas analizando una imagen de entrada.
@@ -457,6 +408,8 @@ def miniframe_estrella(estrellita_, fila, z_):
star = estrellita_[z_[0,fila]-len(ancho)-5:z_[0,fila]+len(ancho)+5, z_[1,fila]-len(ancho)-5:z_[1,fila]+len(ancho)+5]
+ #star = estrellita_[z_[0,fila]-10:z_[0,fila]+10, z_[1,fila]-10:z_[1,fila]+10]
+
#Retorna el array correspondiente a la imagen del miniframe
return radio, star
@@ -510,7 +463,7 @@ def estrellas_recortadas(img_es, intensidad):
while k in range(nz-2):
# 2. Es decir que vuelve aquÃ*
-
+
k += 1
r = abs(z[:,k]-z[:,k+1])
@@ -520,30 +473,35 @@ def estrellas_recortadas(img_es, intensidad):
if r_sum <= 2:
radio, miniestrella = miniframe_estrella(estrellita, k, z)
-
+
lista_estrellas.append(miniestrella)
else:
+
continue # 1. Continua con el ciclo*
- return np.array(lista_estrellas)
+ #lista_estrellas = np.array(lista_estrellas)
+ return lista_estrellas
```
+Utilizando la imagen de 'Estrella 2' para el análisis, se encuentran las siguientes entrellas dentro.
+
```python
lista_estrellas=estrellas_recortadas(estrella_2, 240)
+
fig, axs = plt.subplots(2, 2, figsize=(6,6))
-axs[0, 0].imshow(lista_estrellas[0,:,:], cmap='gray')
+axs[0, 0].imshow(lista_estrellas[0], cmap='gray')
axs[0, 0].set_title('Estrella 1')
-axs[0, 1].imshow(lista_estrellas[1,:,:], cmap='gray')
+axs[0, 1].imshow(lista_estrellas[1], cmap='gray')
axs[0, 1].set_title('Estrella 2')
-axs[1, 0].imshow(lista_estrellas[2,:,:], cmap='gray')
+axs[1, 0].imshow(lista_estrellas[2], cmap='gray')
axs[1, 0].set_title('Estrella 3')
-axs[1, 1].imshow(lista_estrellas[3,:,:], cmap='gray')
+axs[1, 1].imshow(lista_estrellas[3], cmap='gray')
axs[1, 1].set_title('Estrella 4')
# Hide x labels and tick labels for top plots and y ticks for right plots.
@@ -554,170 +512,161 @@ for ax in axs.flat:
-
+
+
+
+
+Para el análisis de las anteriores estrellas se hace uso de la clase, se presenta además el ajuste gaussiano de dichas estrellas
+
+
+```python
+#Conversion a la clase estrella
+
+clase_estrella=[]
+
+for item in lista_estrellas:
+ clase_estrella.append(Estrella_a(item))
+
+```
+
+
+```python
+#Impresión de las imagenes
+
+#Para recordar: p = [a, b, c, x0, y0]
+
+
+p1=np.array([1,0,1,5,5])
+
+graficas=[]
+
+parametros=[]
+
+
+for i in range(0,len(clase_estrella)):
+ uno, dos = clase_estrella[i].ajusteGauss(p1)
+
+ graficas.append(uno)
+ parametros.append(dos)
+
+
+#Impresion de imagenes
+
+fig, axs = plt.subplots(2, 2, figsize=(6,6))
+axs[0, 0].imshow(graficas[0], cmap='gray')
+axs[0, 0].set_title('Estrella 1')
+axs[0, 1].imshow(graficas[1], cmap='gray')
+axs[0, 1].set_title('Estrella 2')
+axs[1, 0].imshow(graficas[2], cmap='gray')
+axs[1, 0].set_title('Estrella 3')
+axs[1, 1].imshow(graficas[3], cmap='gray')
+axs[1, 1].set_title('Estrella 4')
+
+# Hide x labels and tick labels for top plots and y ticks for right plots.
+for ax in axs.flat:
+ ax.label_outer()
+
+```
+
+
+
+
+
+
+
+**Análisis estadistico**
+
+Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:
+
+
+```python
+evaluar=np.array(parametros)
+
+evaluar=evaluar*2*math.sqrt(2*math.log(2))
+
+```
+
+
+```python
+zz=evaluar
+```
+
+
+```python
+#Media
+media=np.mean(zz)
+
+#Mediana
+mediana=np.median(zz)
+
+#Moda
+moda=stat.mode(np.round(zz,3).reshape(-1))
+
+#Desviacion estandar con scipy
+desviacion=stat.stdev(zz.reshape(-1))
+
+#Desviacion estandar con numpy
+desviacion1=zz.std()
+
+print('Mediana: ', mediana)
+print('Media: ', media)
+print('Moda: ', moda)
+print('Desviacion estandar 1: ', desviacion)
+print('Desviacion estandar 2: ', desviacion1)
+```
+
+ Mediana: 2.9331335083105
+ Media: 6.958263965191911
+ Moda: 15.497
+ Desviacion estandar 1: 6.4437838006164565
+ Desviacion estandar 2: 6.280623550702009
+
+
+Para hacer el histograma
+
+
+```python
+#Histograma
+
+plt.figure(figsize=(9,4))
+plt.hist(zz, bins=5, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)
+plt.show()
+```
+
+
+
+
+
+
+
+------------------------------------------------------------------
+
+
+```python
+estrellas_para_estadistica = estrellas_recortadas(imagen_grisss, 230)
+
+```
```python
#Para guardar el notebook a .md
-! jupyter nbconvert --to markdown ENTREGA.ipynb
+! jupyter nbconvert --to markdown Entrega.ipynb
```
- [NbConvertApp] WARNING | pattern 'ENTREGA.ipynb' matched no files
- This application is used to convert notebook files (*.ipynb) to various other
- formats.
-
- WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.
-
- Options
- =======
- The options below are convenience aliases to configurable class-options,
- as listed in the "Equivalent to" description-line of the aliases.
- To see all configurable class-options for some <cmd>, use:
- <cmd> --help-all
-
- --debug
- set log level to logging.DEBUG (maximize logging output)
- Equivalent to: [--Application.log_level=10]
- --generate-config
- generate default config file
- Equivalent to: [--JupyterApp.generate_config=True]
- -y
- Answer yes to any questions instead of prompting.
- Equivalent to: [--JupyterApp.answer_yes=True]
- --execute
- Execute the notebook prior to export.
- Equivalent to: [--ExecutePreprocessor.enabled=True]
- --allow-errors
- Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
- Equivalent to: [--ExecutePreprocessor.allow_errors=True]
- --stdin
- read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
- Equivalent to: [--NbConvertApp.from_stdin=True]
- --stdout
- Write notebook output to stdout instead of files.
- Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
- --inplace
- Run nbconvert in place, overwriting the existing notebook (only
- relevant when converting to notebook format)
- Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
- --clear-output
- Clear output of current file and save in place,
- overwriting the existing notebook.
- Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
- --no-prompt
- Exclude input and output prompts from converted document.
- Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
- --no-input
- Exclude input cells and output prompts from converted document.
- This mode is ideal for generating code-free reports.
- Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True]
- --allow-chromium-download
- Whether to allow downloading chromium if no suitable version is found on the system.
- Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
- --log-level=<Enum>
- Set the log level by value or name.
- Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
- Default: 30
- Equivalent to: [--Application.log_level]
- --config=<Unicode>
- Full path of a config file.
- Default: ''
- Equivalent to: [--JupyterApp.config_file]
- --to=<Unicode>
- The export format to be used, either one of the built-in formats
- ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf',
- 'python', 'rst', 'script', 'slides', 'webpdf'] or a dotted object name that
- represents the import path for an `Exporter` class
- Default: ''
- Equivalent to: [--NbConvertApp.export_format]
- --template=<Unicode>
- Name of the template to use
- Default: ''
- Equivalent to: [--TemplateExporter.template_name]
- --template-file=<Unicode>
- Name of the template file to use
- Default: None
- Equivalent to: [--TemplateExporter.template_file]
- --writer=<DottedObjectName>
- Writer class used to write the results of the conversion
- Default: 'FilesWriter'
- Equivalent to: [--NbConvertApp.writer_class]
- --post=<DottedOrNone>
- PostProcessor class used to write the results of the conversion
- Default: ''
- Equivalent to: [--NbConvertApp.postprocessor_class]
- --output=<Unicode>
- overwrite base name use for output files. can only be used when converting
- one notebook at a time.
- Default: ''
- Equivalent to: [--NbConvertApp.output_base]
- --output-dir=<Unicode>
- Directory to write output(s) to. Defaults to output to the directory of each
- notebook. To recover previous default behaviour (outputting to the current
- working directory) use . as the flag value.
- Default: ''
- Equivalent to: [--FilesWriter.build_directory]
- --reveal-prefix=<Unicode>
- The URL prefix for reveal.js (version 3.x). This defaults to the reveal CDN,
- but can be any url pointing to a copy of reveal.js.
- For speaker notes to work, this must be a relative path to a local copy of
- reveal.js: e.g., "reveal.js".
- If a relative path is given, it must be a subdirectory of the current
- directory (from which the server is run).
- See the usage documentation
- (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-
- slideshow) for more details.
- Default: ''
- Equivalent to: [--SlidesExporter.reveal_url_prefix]
- --nbformat=<Enum>
- The nbformat version to write. Use this to downgrade notebooks.
- Choices: any of [1, 2, 3, 4]
- Default: 4
- Equivalent to: [--NotebookExporter.nbformat_version]
-
- Examples
- --------
-
- The simplest way to use nbconvert is
-
- > jupyter nbconvert mynotebook.ipynb --to html
-
- Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].
-
- > jupyter nbconvert --to latex mynotebook.ipynb
-
- Both HTML and LaTeX support multiple output templates. LaTeX includes
- 'base', 'article' and 'report'. HTML includes 'basic' and 'full'. You
- can specify the flavor of the format used.
-
- > jupyter nbconvert --to html --template lab mynotebook.ipynb
-
- You can also pipe the output to stdout, rather than a file
-
- > jupyter nbconvert mynotebook.ipynb --stdout
-
- PDF is generated via latex
-
- > jupyter nbconvert mynotebook.ipynb --to pdf
-
- You can get (and serve) a Reveal.js-powered slideshow
-
- > jupyter nbconvert myslides.ipynb --to slides --post serve
-
- Multiple notebooks can be given at the command line in a couple of
- different ways:
-
- > jupyter nbconvert notebook*.ipynb
- > jupyter nbconvert notebook1.ipynb notebook2.ipynb
-
- or you can specify the notebooks list in a config file, containing::
-
- c.NbConvertApp.notebooks = ["my_notebook.ipynb"]
-
- > jupyter nbconvert --config mycfg.py
-
- To see all available configurables, use `--help-all`.
-
+ [NbConvertApp] Converting notebook Entrega.ipynb to markdown
+ [NbConvertApp] Support files will be in Entrega_files/
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Making directory Entrega_files
+ [NbConvertApp] Writing 13781 bytes to Entrega.md
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