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@gabraganca
Created August 30, 2017 14:16
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Recipe for fitting gaussians on `vsini` data for differente clusters.
vsini cluster
57.9878 Trumpler 14
65.384 Trumpler 14
171.073 Trumpler 14
38.8181 Trumpler 14
35.236 Trumpler 14
43.879 Trumpler 14
382.417 Trumpler 14
139.456 Trumpler 14
35.8049 Trumpler 14
293.014 Trumpler 14
336.742 Trumpler 14
22.9998 Trumpler 14
219.034 Trumpler 14
186.617 Trumpler 14
211.977 Trumpler 14
94.07 Trumpler 14
140.756 Trumpler 14
129.132 Trumpler 14
111.308 Trumpler 14
193.01 Trumpler 14
159.108 Trumpler 14
20.9764 Trumpler 14
130.669 Trumpler 14
141.495 Trumpler 14
120.19 Trumpler 14
231.247 Trumpler 14
179.272 Trumpler 14
113.719 Trumpler 14
72.8595 Trumpler 14
235.168 Trumpler 14
196.199 Trumpler 14
99.3087 Trumpler 14
70.3562 Trumpler 14
74.0638 Trumpler 14
207.081 Trumpler 14
37.7938 Trumpler 14
250.695 Trumpler 14
172.575 Trumpler 14
208.855 Trumpler 14
107.351 Trumpler 14
90.2165 Trumpler 14
312.491 Trumpler 14
98.9368 Trumpler 14
344.357 Trumpler 14
242.242 Trumpler 14
258.928 Trumpler 14
165.153 Trumpler 14
243.603 Trumpler 14
172.017 Trumpler 14
296.173 Trumpler 14
41.6243 Trumpler 14
109.125 Trumpler 14
93.9298 Trumpler 14
110.262 Trumpler 14
168.077 Trumpler 14
113.935 Trumpler 14
323.501 Trumpler 14
229.752 Trumpler 14
109.714 Trumpler 14
305.175 Trumpler 14
228.607 Trumpler 14
84.5378 Trumpler 14
334.584 Trumpler 14
17.3951 Trumpler 14
49.6526 Trumpler 15
218.736 Trumpler 15
121.704 Trumpler 15
163.86 Trumpler 15
247.526 Trumpler 15
220.189 Trumpler 15
55.1379 Trumpler 15
193.237 Trumpler 15
51.5289 Trumpler 15
120.33 Trumpler 15
105.022 Trumpler 15
121.301 Trumpler 15
179.592 Trumpler 15
203.791 Trumpler 15
11.7328 Trumpler 15
28.578 Trumpler 15
194.89 Trumpler 15
35.6318 Trumpler 15
84.6735 Trumpler 15
67.8514 Trumpler 15
54.9068 Trumpler 15
37.9712 Trumpler 15
231.644 Trumpler 15
52.9393 Trumpler 15
264.651 Trumpler 15
204.193 Trumpler 15
151.018 Trumpler 15
104.398 Trumpler 15
344.167 Trumpler 15
177.027 Trumpler 15
145.815 Trumpler 15
182.701 Trumpler 15
213.454 Trumpler 15
211.753 Trumpler 15
235.28 Trumpler 15
251.058 Trumpler 15
197.726 Trumpler 15
94.1865 Trumpler 15
185.395 Trumpler 15
251.391 Trumpler 15
284.919 Trumpler 15
305.479 Trumpler 15
127.175 Trumpler 15
238.19 Trumpler 15
102.609 Trumpler 15
282.508 Trumpler 15
188.406 Trumpler 15
183.675 Trumpler 15
182.469 Trumpler 15
21.7239 Trumpler 15
165.861 Trumpler 15
15.8274 Trumpler 15
263.703 Trumpler 15
84.7828 Trumpler 16
264.795 Trumpler 16
119.223 Trumpler 16
68.6642 Trumpler 16
81.1297 Trumpler 16
144.554 Trumpler 16
14.7442 Trumpler 16
37.4534 Trumpler 16
187.948 Trumpler 16
79.3175 Trumpler 16
310.702 Trumpler 16
125.249 Trumpler 16
185.065 Trumpler 16
68.1076 Trumpler 16
208.07 Trumpler 16
77.9085 Trumpler 16
60.4719 Trumpler 16
243.608 Trumpler 16
366.12 Trumpler 16
138.191 Trumpler 16
221.767 Trumpler 16
103.04 Trumpler 16
277.35 Trumpler 16
256.706 Trumpler 16
89.7452 Trumpler 16
253.644 Trumpler 16
41.4509 Trumpler 16
190.228 Trumpler 16
67.2087 Trumpler 16
5.82525 Trumpler 16
74.0371 Trumpler 16
33.2878 Trumpler 16
378.31 Trumpler 16
228.679 Trumpler 16
43.2795 Trumpler 16
153.796 Trumpler 16
163.183 Trumpler 16
223.837 Trumpler 16
102.965 Trumpler 16
124.337 Trumpler 16
310.117 Trumpler 16
284.289 Trumpler 16
234.754 Trumpler 16
251.804 Trumpler 16
70.096 Trumpler 16
120.901 Trumpler 16
201.547 Trumpler 16
208.242 Trumpler 16
94.6696 Trumpler 16
340.537 Trumpler 16
314.355 Trumpler 16
154.95 Trumpler 16
308.867 Trumpler 16
186.727 Trumpler 16
225.801 Trumpler 16
115.141 Trumpler 16
66.6807 Trumpler 16
42.9907 Trumpler 16
32.2578 Trumpler 16
126.587 Trumpler 16
322.042 Trumpler 16
119.572 Trumpler 16
66.6517 Trumpler 16
37.0228 Trumpler 16
89.0948 Trumpler 16
148.867 Trumpler 16
130.371 Trumpler 16
324.708 Trumpler 16
131.034 Trumpler 16
300.551 Trumpler 16
193.175 Trumpler 16
241.358 Trumpler 16
122.499 Trumpler 16
123.977 Trumpler 16
143.956 Trumpler 16
338.755 Trumpler 16
97.5558 Trumpler 16
256.615 Trumpler 16
27.9507 Trumpler 16
367.235 Trumpler 16
266.21 Trumpler 16
191.147 Trumpler 16
139.22 Trumpler 16
280.936 Trumpler 16
338.309 Trumpler 16
55.6409 Trumpler 16
213.005 Trumpler 16
52.5811 Trumpler 16
192.032 Trumpler 16
82.42 Trumpler 16
138.656 Trumpler 16
127.219 Trumpler 16
100.896 Trumpler 16
96.2943 Trumpler 16
31.6879 Trumpler 16
253.713 Trumpler 16
179.486 Trumpler 16
118.342 Trumpler 16
246.076 Trumpler 16
70.7138 Trumpler 16
76.6998 Collinder 228
111.232 Collinder 228
38.1101 Collinder 228
116.53 Collinder 228
71.9773 Collinder 228
69.8987 Collinder 228
141.808 Collinder 228
145.273 Collinder 228
105.359 Collinder 228
226.455 Collinder 228
88.3877 Collinder 228
214.917 Collinder 228
163.201 Collinder 228
63.2733 Collinder 228
38.0584 Collinder 228
136.399 Collinder 228
108.286 Collinder 228
137.556 Collinder 228
84.0482 Collinder 228
176.397 Collinder 228
70.8588 Collinder 228
216.073 Collinder 228
208.963 Collinder 228
181.929 Collinder 228
154.666 Collinder 228
259.497 Collinder 228
291.521 Collinder 228
82.8043 Collinder 228
62.0351 Collinder 228
51.5094 Collinder 228
180.649 Collinder 228
63.6883 Collinder 228
33.3162 Collinder 228
79.365 Collinder 228
148.77 Collinder 228
185.94 Collinder 228
81.4509 Collinder 228
53.1167 Collinder 228
200.651 Collinder 228
145.583 Collinder 228
150.861 Collinder 228
265.087 Collinder 228
69.2762 Collinder 228
15.3986 Collinder 228
189.65 Collinder 228
280.941 Collinder 228
40.7776 Collinder 228
369.68 Collinder 228
134.941 Collinder 228
104.527 Collinder 228
217.496 Collinder 228
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fitting Gaussians"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load necessary packages/libraries."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import stats\n",
"import pandas as pd\n",
"from sklearn.mixture import GaussianMixture\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define functions."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# General functions\n",
"\n",
"def fit_gaussian(data, n_fit):\n",
" \"\"\"\n",
" Fit gaussian distributions using Gaussian Mixture Model.\n",
"\n",
" For more information, please check:\n",
"\n",
" http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html\n",
"\n",
" Parameters\n",
" ----------\n",
"\n",
" data: list;\n",
" 1-dimensional list with data to fit.\n",
"\n",
" n_gauss: int;\n",
" Number of gaussian to fit.\n",
"\n",
" Returns\n",
" -------\n",
"\n",
" Dictionary with `weight`, `mean` and `stdev` (standard\n",
" deviation) of the distribtuions.\n",
"\n",
" \"\"\"\n",
"\n",
" gmm = GaussianMixture(n_components=n_fit, covariance_type='full')\n",
" _ = gmm.fit(np.reshape(data, (-1,1)))\n",
"\n",
" if gmm.converged_:\n",
" return {'weight':gmm.weights_,\n",
" 'mean':gmm.means_.flatten(),\n",
" 'stdev':np.sqrt(gmm.covariances_.flatten())}\n",
" else:\n",
" raise RuntimeError('It did not converged.')\n",
"\n",
" \n",
"def plot_dist(data, bins, n_fit):\n",
" \"\"\"\n",
" Plot the distribution and fit a gaussian.\n",
"\n",
" Parameters\n",
" ----------\n",
"\n",
" data: list;\n",
" 1-dimensional list with data.\n",
" \n",
" bins: list;\n",
" Position of the bins.\n",
"\n",
" n_fit: int;\n",
" Number of gaussians to fit.\n",
" \"\"\"\n",
" # Fit normald distributions\n",
" dist_fit = fit_gaussian(data, n_fit)\n",
"\n",
" # set function to construct normal distribution PDF\n",
" normpdf = lambda x, weight, mean, std: weight*stats.norm.pdf(x, loc=mean,\n",
" scale=std)\n",
"\n",
" # Plot\n",
" fig, ax = plt.subplots()\n",
"\n",
" ax.hist(data, bins=bins, normed=True, zorder=3, ec='w')\n",
"\n",
" X = np.linspace(bins[0], bins[-1], 500)\n",
"\n",
" for i in range(n_fit):\n",
" mean = dist_fit['mean'][i]\n",
" stdev = dist_fit['stdev'][i]\n",
" weight = dist_fit['weight'][i]\n",
"\n",
" label_text='{}={:.2f}, {}={:.2f}'.format('$\\mu$', mean, '$\\sigma$',\n",
" stdev)\n",
"\n",
" ax.plot(X, normpdf(X, weight, mean, stdev),\n",
" linewidth=5, label=label_text, zorder=5)\n",
"\n",
" ax.set_ylabel('Normalized Frequency')\n",
"\n",
" ax.legend(loc='upper right')\n",
" return fig, ax\n",
"\n",
"\n",
"# Specific problem functions\n",
"\n",
"def plot_dist_cluster(cluster, bins, n_fit):\n",
" \"\"\"\n",
" Special function to plot the vsini of the cluster stars.\n",
" \n",
" This was especially built for this problem.\n",
" \n",
" \"\"\"\n",
" \n",
" fig, ax = plot_dist(df.query(\"cluster=='{}'\".format(cluster))['vsini'].values, bins, n_fit)\n",
" ax.set_title(cluster, fontsize=12) \n",
" ax.set_xlabel(r'v$\\sin i$ (km/s)')\n",
" ax.set_xlim(xmin=0)\n",
" ax.legend(title='Fitted Gaussian')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>vsini</th>\n",
" <th>cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>57.9878</td>\n",
" <td>Trumpler 14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>65.3840</td>\n",
" <td>Trumpler 14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>171.0730</td>\n",
" <td>Trumpler 14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>38.8181</td>\n",
" <td>Trumpler 14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>35.2360</td>\n",
" <td>Trumpler 14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" vsini cluster\n",
"0 57.9878 Trumpler 14\n",
"1 65.3840 Trumpler 14\n",
"2 171.0730 Trumpler 14\n",
"3 38.8181 Trumpler 14\n",
"4 35.2360 Trumpler 14"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"data.csv\")\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check basic statsics on plots:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>cluster</th>\n",
" <th>Collinder 228</th>\n",
" <th>Trumpler 14</th>\n",
" <th>Trumpler 15</th>\n",
" <th>Trumpler 16</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>51.000000</td>\n",
" <td>64.000000</td>\n",
" <td>53.000000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>135.389973</td>\n",
" <td>159.875536</td>\n",
" <td>159.041226</td>\n",
" <td>163.476708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>77.863431</td>\n",
" <td>93.981950</td>\n",
" <td>84.729966</td>\n",
" <td>96.760736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>15.398600</td>\n",
" <td>17.395100</td>\n",
" <td>11.732800</td>\n",
" <td>5.825250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>71.418050</td>\n",
" <td>93.001475</td>\n",
" <td>94.186500</td>\n",
" <td>82.097425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>134.941000</td>\n",
" <td>141.125500</td>\n",
" <td>179.592000</td>\n",
" <td>138.938000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>183.934500</td>\n",
" <td>228.893250</td>\n",
" <td>218.736000</td>\n",
" <td>241.920500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>369.680000</td>\n",
" <td>382.417000</td>\n",
" <td>344.167000</td>\n",
" <td>378.310000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"cluster Collinder 228 Trumpler 14 Trumpler 15 Trumpler 16\n",
"count 51.000000 64.000000 53.000000 100.000000\n",
"mean 135.389973 159.875536 159.041226 163.476708\n",
"std 77.863431 93.981950 84.729966 96.760736\n",
"min 15.398600 17.395100 11.732800 5.825250\n",
"25% 71.418050 93.001475 94.186500 82.097425\n",
"50% 134.941000 141.125500 179.592000 138.938000\n",
"75% 183.934500 228.893250 218.736000 241.920500\n",
"max 369.680000 382.417000 344.167000 378.310000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cluster_grouped_df = df.groupby('cluster')['vsini']\n",
"\n",
"cluster_grouped_df.describe().T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot them:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trumpler 14"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ejz76CBHZ6fcrLVe/38/s2bNZv349vXr1Yu7cuTH7iv766y/OOussHn30UWrVqgXA+PHj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\n7uBz+IoNEf+XZnFX6PwUZWW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V5gyY9w58+QisXlBi84BEOdE/kxP9M9mg1Xg/chhjI0cx\nQ/dH7W/pcmGFxBhTOfkD0L4PtOsNP30AXz7s3MlVitqyhf6BSfQPTGK11uaTyMFMiB7C19E2FBBM\nUuJVjxUSY0zl5vNB657Qqgf88g25zw7jZN80glL6vCINZINTVJjEJs3mq2g7pkTbwdpWUHdf584x\nExcrJMaYqkEEmh3O1aGraMA6+vk/o3/gU/aSdTvdtKZs5WT/dE72T4f/Pg+1m8K+x0DOUdDkECss\nO2GFxBhT5axmDx6PnMkTkTM40jeXXv6v6O6bTjXZFt8ONvwCs152FoBq9ZyC0uQQaNwJ9jwQajRI\n3BeoZKyQGGOqrCg+voy258toe24jn26+GZzmn8qRvrlkyi48Z7JlLfz8kbNsV70h7NUO9mzrFJb6\nLaDufpBdp/y/SAVnhcQYkxa2kMW70aN4N3oU1dnKMb45nOSfwXG+WdSS3RhRePMqWDTJWQrLrgv1\n9nOKSt19YY9mUKsR1GrsDPmSUa18vlAFYoXEGJN2NpPNB9HD+CB6GEHCdJA8jvLP5UjfXA7xLwIt\nvaO+VFv/hBV/worpsddn7+EUlVqNnHHFqtV3Lp1Vd/+tVh+q1XVeZ9asFH0zVkiMMWktRIAZ2ooZ\n4VY8Sm+WDj0alk2BZVOdYvDbLGcI+/KydZ2zrJwbR2OBzFpOQSlpCVaDYHahpRoEsorGGx+c0IKU\n0EIiIt2BxwA/8Kyq3l9sfSbwEnAwsBY4R1WXuutuAQYCEeBqVZ0Qzz6NMaZMsmrBASc7C0C4AFb+\nACtmwK/fOQVg9U8QTcZT8grbNjhLWdy5vnzSKUHCComI+IGRwInACmC6iOSq6vxCzQYC61S1hYj0\nBR4AzhGRNkBfoC3QCJgoIvu72+xsn8YYU34CGc5f9I0P/jsWLoC1C+GPuU6RWbMQ1i5ypgNOSoHZ\nBcFqCb88lsgzki5AnqouBhCR0cDpQOFf+qcDQ93XbwFPiIi48dGqug1YIiJ57v6IY5/GGJNYgQz3\nbq22wDl/xyNh2LAc/lz897LxV9j4m7Ns+h00muRcsxL+EaIJmq5SRHoD3VX1Yvf9+cChqjq4UJu5\nbpsV7vtFwKE4xeUbVX3FjY8CPnQ3K3WfhfZ9KXCp+7YdEM8FyXRQH1iT6iQqCDsWf7Nj8Tc7Fo5m\nqhrXwzKJPCOJdS5VvGqV1KakeKwR1mJWQlV9GngaQERmqGrnklNNH3Ys/mbH4m92LP5mx2LXJXLo\nyxXAPoXeNwF+K6mNiASA2sCfpWwbzz6NMcYkUSILyXSgpYg0F5EMnM7z3GJtcoEB7uvewCR1rrXl\nAn1FJFNEmgMtgWlx7tMYY0wSJezSlqqGRWQwMAHnVt3nVHWeiAwHZqhqLjAKeNntTP8TpzDgthuD\n04keBgapOk8IxdpnHOk8Xc5frzKzY/E3OxZ/s2PxNzsWuyhhne3GGGPSg00PZowxpkyskBhjjCmT\nKl1IRKS7iPwkInkiMiTV+SSaiDwnIqvc53O2x+qKyCcistD9dw83LiLyuHtsvheRTqnLvPyJyD4i\n8pmILBCReSJyjRtPu+MhIlkiMk1E5rjHYpgbby4i37rH4g33Bhbcm1zecI/FtyKSk8r8E0FE/CIy\nS0TGu+/T9liUhypbSAoN0XIy0Abo5w69UpW9AHQvFhsCfKqqLYFP3ffgHJeW7nIp8H9JyjFZwsD1\nqtoaOAwY5P73T8fjsQ04XlU7AB2B7iJyGM6QRCPcY7EOZ8giKDR0ETDCbVfVXAMsKPQ+nY9FmVXZ\nQkKhIVpUtQDYPpxKlaWqX+Dc/VbY6cCL7usXgTMKxV9SxzdAHRHZOzmZJp6q/q6q37mvN+H80mhM\nGh4P9zv95b4NuosCx+MMTQTeY7H9GL0FnOAOXVQliEgToAfwrPteSNNjUV6qciFpDCwv9H6FG0s3\ne6rq7+D8cgUauvG0OT7u5YiDgG9J0+PhXsqZDawCPgEWAetVdfs0gYW/745j4a7fANRLbsYJ9Shw\nE7B90Kt6pO+xKBdVuZDEM0RLOkuL4yMiNYC3gWtVdWNpTWPEqszxUNWIqnbEGQ2iC9A6VjP33yp7\nLESkJ7BKVWcWDsdoWuWPRXmqyoXEhlNxrNx+icb9d5Ubr/LHR0SCOEXkVVV9xw2n7fEAUNX1wGSc\nfqM67tBEUPT7ljR0UVVwJHCaiCzFudx9PM4ZSjoei3JTlQuJDafiKDwMzQBgXKH4Be7dSocBG7Zf\n8qkK3OvYo4AFqvpIoVVpdzxEpIGI1HFfZwP/wOkz+gxnaCLwHotYQxdVeqp6i6o2UdUcnN8Jk1T1\nXNLwWJQrVa2yC3AK8DPO9eBbU51PEr7v68DvQAjnL6mBONdzPwUWuv/WddsKzl1ti4AfgM6pzr+c\nj8VROJcgvgdmu8sp6Xg8gPbALPdYzAXucOP74oxhlwe8CWS68Sz3fZ67ft9Uf4cEHZdjgfF2LMq+\n2D5swUwAAAM3SURBVBApxhhjyqQqX9oyxhiTBFZIjDHGlIkVEmOMMWVihcQYY0yZWCExxhhTJlZI\njDHGlIkVEmOMMWVihcSY3SQiU+Noc8T2+T+KxbNF5HN3MMWcwnPI7GYuT4nIkSWsyxCRLwoNAWJM\nufr/9u4nRKsqjOP494doUgRB6cKVSFGhg2SYIIUIgrkOGtCCalfLaFOCuhNx1cKVLg0GCnGj+A9a\npW3SYEZTN7bJFgOpoKQTMz8X50xzXrnDvOMdcfP7rN73vfec88ws3mfOvXeeJ4kk4inZ3jrEORdt\n7+849AVwwvb0EoWzBfh1nhimKP/FP7pEa0UMSCKJqCQdkvRV8/6ApL2STtXughOSRpvj9+tu4g9J\nR2v3wXO1ntXsOT9Ker9juT3M1XNqY1hXO/dtlnRd0rG67g+Sdkj6pXbxe68Z8zZw0/a0pJfmifdk\nXTNiySWRRMwZY/Cv9o8pNctu295oewNwpmPcG8AR2+uBu8BHzbENlNpd/6tFRNfZ/vOJz9+kVCv+\nHJgEXge+p9TKegvYTakh9g3wXTN0VxPXh/PEOwFsXuDnj3gqSSQRle0rwGpJayRtpLRcvQTsqLuV\nD2zf6xh6y/bv9fVvwFoovdKB5R1jXqMknNYqyg7lk2auW7bHbc8AVyktgk1JTGubsTuZSxjjXfHW\nS2hTkl4e+hcSMaQkkohBP1HKhY8CY7ZvAu9SvqAPStrXMeZR83oamL2pvR641nH+v5Sqsq17lE58\n7Q3zdt6Z5v3M7BqSXgResX0bYIF4XwAedsQT0Uue4ogYNAYcpewatklaA/xj+7ik+8Bni5hrhFK6\nfYDtO/VprZW2Z7/Ypyh9ws/WdRZ8IqzaTumlAcB88Up6FZi0/d8i4o8YShJJRMP21Xr55y/bf0va\nCRyWNEPp8/LlIqYbofSJ73KOcr/jQrP2g9oK9jzlUtcwdlF2Ue2aXfFuB04PH3rE8NKPJOI5kPQO\n8LXtT3vOcxnYstBOQ9IJ4FvbN/qsF9El90ginoN6Y/9nSct6zrNpiCSyAjiZJBLPSnYkERHRS3Yk\nERHRSxJJRET0kkQSERG9JJFEREQvSSQREdFLEklERPTyGJApeMEAn15EAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f791c8f7748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dist_cluster(\"Trumpler 14\", range(0,500,25),1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trumpler 15"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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OHMiCBQvC+kyqmxW2MsbUiOXLlxc1HABLly7lsssuA0J/k+/duzcpKSlF395HjhzJgw8+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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f78d1907d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dist_cluster(\"Trumpler 15\", range(0,500, 25), 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trumpler 16"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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TmCo7UUTeJgGxm9byFnQmsC8hfvDZbEW227JsMBiSl3Cmtn4EGgNnqeoIVf1Y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\n2qLs4RLZ42gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f78cbfc4a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dist_cluster(\"Trumpler 16\", range(0,500,25), 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Collinder 228"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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RFLXtC0dd7o2vX+zMY2KMialI7toaBjyLM+puJ2Ccqt4Q7cRM5I5PWspVfu9o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\niYiITpJIIiKikySSiIjoJIkkIiI6+Qc4knZ/Zn+KLwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f78cbc89e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dist_cluster(\"Collinder 228\", range(0,500,25), 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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