diff --git a/Entrega.ipynb b/Entrega.ipynb
index 4ee2a3fbc11f71e2a0bb7160d02e32a00ec0d08f..c2679f600f5580b73186d78e5fecd665838c61be 100644
--- a/Entrega.ipynb
+++ b/Entrega.ipynb
@@ -113,7 +113,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7fcc33b1e7b8>"
+ "<matplotlib.image.AxesImage at 0x7f64067b77b8>"
]
},
"execution_count": 4,
@@ -312,7 +312,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7fcc33a172b0>"
+ "<matplotlib.image.AxesImage at 0x7f64067006d8>"
]
},
"execution_count": 8,
@@ -830,7 +830,8 @@
"clase_estrella=[]\n",
"\n",
"for item in lista_estrellas:\n",
- " clase_estrella.append(Estrella_a(item))\n"
+ " clase_estrella.append(Estrella_a(item))\n",
+ "\n"
]
},
{
@@ -901,7 +902,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:"
+ "Para hacer el análisis estadÃstico se analiza toda la imagen. Se observan unicamente las estrellas con intensidad mayor a 250. <br>\n",
+ "\n",
+ "Para hacer esto se crea la función _analisis_"
]
},
{
@@ -910,19 +913,137 @@
"metadata": {},
"outputs": [],
"source": [
- "evaluar=np.array(parametros)\n",
+ "def analisis(imagen_grisss):\n",
+ "\n",
+ " #Haciendo el análisis para toda la imagen\n",
+ "\n",
+ " estrellas_para_estadistica = estrellas_recortadas(imagen_grisss, 250)\n",
+ "\n",
+ " #Conversion a la clase estrella\n",
+ "\n",
+ " clase_estrella_todas=[]\n",
+ "\n",
+ "\n",
+ " for item in estrellas_para_estadistica:\n",
+ " clase_estrella_todas.append(Estrella_a(item))\n",
+ "\n",
+ " #Recoleccion de parámetros\n",
+ "\n",
+ " p1=np.array([1,0,1,5,5]) #Para recordar: p = [a, b, c, x0, y0]\n",
+ "\n",
+ " parametros_todas=[]\n",
+ "\n",
+ " for i in range(0,len(clase_estrella_todas)):\n",
+ " uno, dos = clase_estrella_todas[i].ajusteGauss(p1)\n",
+ "\n",
+ " parametros_todas.append(dos)\n",
+ "\n",
+ "\n",
+ " print('En total se estan analizando '+str(len(clase_estrella_todas))+' estrellas')\n",
+ "\n",
+ " #Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:\n",
"\n",
+ " evaluar=np.array(parametros_todas)\n",
"\n",
- "FWHM=evaluar*2*math.sqrt(2*math.log(2))\n"
+ "\n",
+ " FWHM=evaluar*2*math.sqrt(2*math.log(2))\n",
+ "\n",
+ " zz=FWHM\n",
+ "\n",
+ " #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",
+ " #moda=max(set(np.round(zz,2)), key=list(np.round(zz,2)).count)\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(' ')\n",
+ " print('Mediana: ', mediana)\n",
+ " print('Media: ', media)\n",
+ " #print('Moda: ', moda)\n",
+ " print('Desviacion estandar 1: ', desviacion)\n",
+ " print('Desviacion estandar 2: ', desviacion1)\n",
+ "\n",
+ " #Histograma\n",
+ "\n",
+ " plt.figure(figsize=(9,4))\n",
+ " plt.hist(zz, bins=20, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)\n",
+ " plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/vargass/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py:475: RuntimeWarning: Number of calls to function has reached maxfev = 1200.\n",
+ " warnings.warn(errors[info][0], RuntimeWarning)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "En total se estan analizando 33 estrellas\n",
+ " \n",
+ "Mediana: 1.4018568345763582\n",
+ "Media: -60.02889934017261\n",
+ "Desviacion estandar 1: 257.23862885662066\n",
+ "Desviacion estandar 2: 256.4579331901813\n"
+ ]
+ },
+ {
+ "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": [
- "zz=FWHM"
+ "analisis(imagen_grisss)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Analisis para R,G,B independientemente**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Para hacer el análisis estadÃstico para R,G y B independientemente se analiza toda la imagen de cada una de ellas por separado. Se observan unicamente las estrellas con intensidad mayor a 245.\n",
+ "\n",
+ "(Se utiliza la función creada anteriormente)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "_PARA ROJO_"
]
},
{
@@ -934,54 +1055,58 @@
"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"
+ "En total se estan analizando 45 estrellas\n",
+ " \n",
+ "Mediana: 1.46501995989867\n",
+ "Media: -24.20043926725717\n",
+ "Desviacion estandar 1: 159.60019232223172\n",
+ "Desviacion estandar 2: 159.24513027547198\n"
]
+ },
+ {
+ "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": [
- "#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)"
+ "analisis(R)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Para hacer el histograma"
+ "_PARA VERDE_"
]
},
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "scrolled": true
- },
+ "metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "En total se estan analizando 32 estrellas\n",
+ " \n",
+ "Mediana: 1.4056342445779744\n",
+ "Media: -124.0466362617896\n",
+ "Desviacion estandar 1: 654.9121084711437\n",
+ "Desviacion estandar 2: 652.8623002867687\n"
+ ]
+ },
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 648x288 with 1 Axes>"
]
@@ -993,58 +1118,55 @@
}
],
"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()"
+ "analisis(G)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "------------------------------------------------------------------"
+ "_PARA AZUL_"
]
},
{
"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] 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"
+ "En total se estan analizando 45 estrellas\n",
+ " \n",
+ "Mediana: 1.3063649806304618\n",
+ "Media: -4233247.385112735\n",
+ "Desviacion estandar 1: 45270472.137159556\n",
+ "Desviacion estandar 2: 45169759.059932515\n"
]
+ },
+ {
+ "data": {
+ "image/png": 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5yTmT5382yVFJdi+3T59FAgCHmap6f5JfzNKZiO8meUOSjyd5e5KTkxyZ5APd/UdVdUaSdybZnKULPl/T3Vcu+xoCAwAYzVskAMBwAgMAGE5gAADDCQwAYDiBAQAMJzAAgOEEBgAw3P8Dp59v2sRLpm4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 648x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "#Para guardar el notebook a .md\n",
- "! jupyter nbconvert --to markdown Entrega.ipynb"
+ "analisis(B)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Se puede observar que el azul es el que tiene más dispersión de datos, seguido del verde y luego el rojo. Sin embargo, la dispersión presentada por la imagen completa, no es tan grande como se ve con el azul, ni tan pequeña como se ve con el rojo."
]
}
],
diff --git a/Entrega.md b/Entrega.md
index d67512a2427f5be8d6302a176ffd7b5d801799ea..95057b49a7591b1b386d2646d3f8b26408820d2f 100644
--- a/Entrega.md
+++ b/Entrega.md
@@ -57,7 +57,7 @@ plt.imshow(imagen)
- <matplotlib.image.AxesImage at 0x7f38c3d63940>
+ <matplotlib.image.AxesImage at 0x7fc8d32b48d0>
@@ -187,7 +187,7 @@ plt.imshow(imagen_grisss, cmap='gray')
- <matplotlib.image.AxesImage at 0x7f38c3c5b198>
+ <matplotlib.image.AxesImage at 0x7fc8d31f9f98>
@@ -527,6 +527,7 @@ clase_estrella=[]
for item in lista_estrellas:
clase_estrella.append(Estrella_a(item))
+
```
@@ -555,7 +556,6 @@ for i in range(0,len(clase_estrella)):
fig, axs = plt.subplots(2, 2, figsize=(6,6))
-<<<<<<< HEAD
axs[0, 0].imshow(graficas[0], cmap='gray')
axs[0, 0].set_title('Estrella 1')
axs[0, 1].imshow(graficas[1], cmap='gray')
@@ -579,19 +579,56 @@ for ax in axs.flat:
**Análisis estadistico**
+
+```python
+
+```
+
+
+```python
+#Haciendo el análisis para toda la imagen
+
+estrellas_para_estadistica = estrellas_recortadas(imagen_grisss, 245)
+
+#Conversion a la clase estrella
+
+clase_estrella_todas=[]
+
+
+for item in estrellas_para_estadistica:
+ clase_estrella_todas.append(Estrella_a(item))
+
+#Recoleccion de parámetros
+
+p1=np.array([1,0,1,5,5]) #Para recordar: p = [a, b, c, x0, y0]
+
+parametros_todas=[]
+
+print(len(clase_estrella_todas))
+
+for i in range(0,len(clase_estrella_todas)):
+ uno, dos = clase_estrella_todas[i].ajusteGauss(p1)
+
+ parametros_todas.append(dos)
+```
+
+ 54
+
+
Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:
```python
-evaluar=np.array(parametros)
+evaluar=np.array(parametros_todas)
+
-evaluar=evaluar*2*math.sqrt(2*math.log(2))
+FWHM=evaluar*2*math.sqrt(2*math.log(2))
```
```python
-zz=evaluar
+zz=FWHM
```
@@ -618,58 +655,45 @@ 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
+ StatisticsError Traceback (most recent call last)
+ <ipython-input-25-0b358d0ceb36> in <module>
+ 6
+ 7 #Moda
+ ----> 8 moda=stat.mode(np.round(zz,3).reshape(-1))
+ 9
+ 10 #Desviacion estandar con scipy
-```python
-#Histograma
-plt.figure(figsize=(9,4))
-plt.hist(zz, bins=5, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)
-plt.show()
-```
+ /usr/lib/python3.7/statistics.py in mode(data)
+ 504 elif table:
+ 505 raise StatisticsError(
+ --> 506 'no unique mode; found %d equally common values' % len(table)
+ 507 )
+ 508 else:
-
-
-
+ StatisticsError: no unique mode; found 3 equally common values
-------------------------------------------------------------------
+Para hacer el histograma
```python
-estrellas_para_estadistica = estrellas_recortadas(imagen_grisss, 230)
+#Histograma
+plt.figure(figsize=(9,4))
+plt.hist(zz, bins=5, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)
+plt.show()
```
+------------------------------------------------------------------
+
```python
#Para guardar el notebook a .md
! jupyter nbconvert --to markdown Entrega.ipynb
```
-
- [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
-
-=======
->>>>>>> 0f2da7af2bf4815482fe5f2f1fa9eb1e200e0f1f