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larrybradley/imutils_demo.ipynb

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imutils_demo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from photutils.datasets import make_4gaussians_image, make_100gaussians_image\n",
"img1 = make_4gaussians_image()\n",
"img2 = make_100gaussians_image()\n",
"\n",
"from astropy.nddata import NDData\n",
"nd1 = NDData(img1)\n",
"nd2 = NDData(img2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import imutils"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# imstats"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<Table masked=False length=1>\n",
"<table id=\"table4360585936\">\n",
"<thead><tr><th>npixels</th><th>mean</th><th>std</th><th>min</th><th>max</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>20000</td><td>8.81095902955</td><td>18.1585385789</td><td>-13.7267793867</td><td>216.044619962</td></tr>\n",
"</table>"
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"text/plain": [
"<Table masked=False length=1>\n",
"npixels mean std min max \n",
" int64 float64 float64 float64 float64 \n",
"------- ------------- ------------- -------------- -------------\n",
" 20000 8.81095902955 18.1585385789 -13.7267793867 216.044619962"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# image statistics on NDData.data\n",
"imutils.imstats(nd1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4360496656\">\n",
"<thead><tr><th>npixels</th><th>mean</th><th>std</th><th>min</th><th>max</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>19605</td><td>6.66738139372</td><td>8.11970267739</td><td>-13.7267793867</td><td>60.2231826226</td></tr>\n",
"</table>"
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"text/plain": [
"<Table masked=False length=1>\n",
"npixels mean std min max \n",
" int64 float64 float64 float64 float64 \n",
"------- ------------- ------------- -------------- -------------\n",
" 19605 6.66738139372 8.11970267739 -13.7267793867 60.2231826226"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sigma-clipped image statistics\n",
"imutils.imstats(nd1, sigma=3.)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4383014864\">\n",
"<thead><tr><th>npixels</th><th>mean</th><th>std</th><th>min</th><th>max</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>19497</td><td>6.40032721609</td><td>7.30083960368</td><td>-13.7267793867</td><td>49.9478086124</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=1>\n",
"npixels mean std min max \n",
" int64 float64 float64 float64 float64 \n",
"------- ------------- ------------- -------------- -------------\n",
" 19497 6.40032721609 7.30083960368 -13.7267793867 49.9478086124"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# NDData masks are used when calculating statistics\n",
"nd1.mask = (nd1.data > 50.)\n",
"imutils.imstats(nd1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: The data array contains at least one unmasked invalid value (NaN or inf). These values will be automatically masked. [imutils.core]\n",
"WARNING:astropy:The data array contains at least one unmasked invalid value (NaN or inf). These values will be automatically masked.\n"
]
},
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4360585808\">\n",
"<thead><tr><th>npixels</th><th>mean</th><th>std</th><th>min</th><th>max</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>19999</td><td>8.81146521406</td><td>18.1588514591</td><td>-13.7267793867</td><td>216.044619962</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=1>\n",
"npixels mean std min max \n",
" int64 float64 float64 float64 float64 \n",
"------- ------------- ------------- -------------- -------------\n",
" 19999 8.81146521406 18.1588514591 -13.7267793867 216.044619962"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# any unmasked invalid values (NaN or inf) are automatically masked\n",
"img1[40, 50] = np.nan\n",
"nd1 = NDData(img1)\n",
"imutils.imstats(nd1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4383042640\">\n",
"<thead><tr><th>npixels</th><th>mean</th><th>std</th><th>min</th><th>max</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>19999</td><td>8.81146521406</td><td>18.1588514591</td><td>-13.7267793867</td><td>216.044619962</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=1>\n",
"npixels mean std min max \n",
" int64 float64 float64 float64 float64 \n",
"------- ------------- ------------- -------------- -------------\n",
" 19999 8.81146521406 18.1588514591 -13.7267793867 216.044619962"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# if the invalid value is already masked by NDData.mask, then no warning is issued\n",
"mask = np.zeros_like(img1, dtype=bool)\n",
"mask[40, 50] = True\n",
"nd1 = NDData(img1, mask=mask)\n",
"imutils.imstats(nd1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4383042512\">\n",
"<thead><tr><th>npixels</th><th>nrejected</th><th>mean</th><th>median</th><th>mode</th><th>std</th><th>mad_std</th><th>biweight_location</th><th>biweight_midvariance</th><th>min</th><th>max</th><th>skew</th><th>kurtosis</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>19999</td><td>1</td><td>8.81146521406</td><td>5.78293498498</td><td>-0.274125473181</td><td>18.1588514591</td><td>5.63190476905</td><td>5.51707472793</td><td>5.63506534226</td><td>-13.7267793867</td><td>216.044619962</td><td>6.25217472372</td><td>49.0433374289</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=1>\n",
"npixels nrejected mean ... max skew kurtosis \n",
" int64 int64 float64 ... float64 float64 float64 \n",
"------- --------- ------------- ... ------------- ------------- -------------\n",
" 19999 1 8.81146521406 ... 216.044619962 6.25217472372 49.0433374289"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the returned statistics are specified with a \"columns\" list\n",
"# all of the currently available statistics\n",
"columns = ['npixels', 'nrejected', 'mean', 'median', 'mode', 'std',\n",
" 'mad_std', 'biweight_location', 'biweight_midvariance',\n",
" 'min', 'max', 'skew', 'kurtosis']\n",
"imutils.imstats(nd1, columns=columns)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4408891536\">\n",
"<thead><tr><th>exptime</th><th>gain</th><th>npixels</th><th>mean</th><th>std</th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>1200.0</td><td>2.0</td><td>19999</td><td>8.81146521406</td><td>18.1588514591</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=1>\n",
"exptime gain npixels mean std \n",
"float64 float64 int64 float64 float64 \n",
"------- ------- ------- ------------- -------------\n",
" 1200.0 2.0 19999 8.81146521406 18.1588514591"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the 'columns' input can also include NDData.meta (i.e header) keys\n",
"nd1.meta['exptime'] = 1200.\n",
"nd1.meta['gain'] = 2.\n",
"columns = ['exptime', 'gain', 'npixels', 'mean', 'std']\n",
"imutils.imstats(nd1, columns=columns)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4383041872\">\n",
"<thead><tr><th>exptime</th><th>gain</th><th>readnoise</th><th>npixels</th><th>mean</th><th>std</th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>object</th><th>int64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>1200.0</td><td>2.0</td><td>None</td><td>19999</td><td>8.81146521406</td><td>18.1588514591</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=1>\n",
"exptime gain readnoise npixels mean std \n",
"float64 float64 object int64 float64 float64 \n",
"------- ------- --------- ------- ------------- -------------\n",
" 1200.0 2.0 None 19999 8.81146521406 18.1588514591"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the output column is None if the key is not in NDData.meta\n",
"columns = ['exptime', 'gain', 'readnoise', 'npixels', 'mean', 'std']\n",
"imutils.imstats(nd1, columns=columns)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=2&gt;\n",
"<table id=\"table4360587600\">\n",
"<thead><tr><th>npixels</th><th>mean</th><th>std</th><th>min</th><th>max</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>19999</td><td>8.81146521406</td><td>18.1588514591</td><td>-13.7267793867</td><td>216.044619962</td></tr>\n",
"<tr><td>150000</td><td>5.50788163342</td><td>3.03659689936</td><td>-3.30171725986</td><td>111.072139628</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=2>\n",
"npixels mean std min max \n",
" int64 float64 float64 float64 float64 \n",
"------- ------------- ------------- -------------- -------------\n",
" 19999 8.81146521406 18.1588514591 -13.7267793867 216.044619962\n",
" 150000 5.50788163342 3.03659689936 -3.30171725986 111.072139628"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# can input a list of NDData objects\n",
"imutils.imstats([nd1, nd2])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1014, 1014)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read a FITS extension into a NDData object\n",
"fn = 'ibug0pgcq_flt.fits'\n",
"nd3 = imutils.basic_fits_to_nddata(fn, exten=1)\n",
"nd3.data.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"&lt;Table masked=False length=1&gt;\n",
"<table id=\"table4408891344\">\n",
"<thead><tr><th>npixels</th><th>mean</th><th>std</th><th>min</th><th>max</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
"<tr><td>1028196</td><td>0.491587</td><td>21.905</td><td>-9105.49</td><td>13883.8</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=False length=1>\n",
"npixels mean std min max \n",
" int64 float32 float32 float32 float32\n",
"------- -------- ------- -------- -------\n",
"1028196 0.491587 21.905 -9105.49 13883.8"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imutils.imstats(nd3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# minmax"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-9105.4902, 13883.807)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ndarray input\n",
"imutils.minmax(nd3.data)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: The following attributes were set on the data object, but will be ignored by the function: meta [astropy.nddata.decorators]\n",
"WARNING:astropy:The following attributes were set on the data object, but will be ignored by the function: meta\n"
]
},
{
"data": {
"text/plain": [
"(-9105.4902, 13883.807)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# NDData input\n",
"imutils.minmax(nd3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# listpixels"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"&lt;Table masked=False length=15&gt;\n",
"<table id=\"table4408892752\">\n",
"<thead><tr><th>x</th><th>y</th><th>value</th></tr></thead>\n",
"<thead><tr><th>int64</th><th>int64</th><th>float64</th></tr></thead>\n",
"<tr><td>89</td><td>58</td><td>199.200972315</td></tr>\n",
"<tr><td>90</td><td>58</td><td>207.01334291</td></tr>\n",
"<tr><td>91</td><td>58</td><td>195.240059682</td></tr>\n",
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"<tr><td>91</td><td>59</td><td>210.512092422</td></tr>\n",
"<tr><td>89</td><td>60</td><td>201.415548635</td></tr>\n",
"<tr><td>90</td><td>60</td><td>216.044619962</td></tr>\n",
"<tr><td>91</td><td>60</td><td>214.319023132</td></tr>\n",
"<tr><td>89</td><td>61</td><td>209.483749377</td></tr>\n",
"<tr><td>90</td><td>61</td><td>211.040683789</td></tr>\n",
"<tr><td>91</td><td>61</td><td>206.253832044</td></tr>\n",
"<tr><td>89</td><td>62</td><td>195.872102692</td></tr>\n",
"<tr><td>90</td><td>62</td><td>199.675268885</td></tr>\n",
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"</table>"
],
"text/plain": [
"<Table masked=False length=15>\n",
" x y value \n",
"int64 int64 float64 \n",
"----- ----- -------------\n",
" 89 58 199.200972315\n",
" 90 58 207.01334291\n",
" 91 58 195.240059682\n",
" 89 59 205.135838352\n",
" 90 59 208.919557902\n",
" 91 59 210.512092422\n",
" 89 60 201.415548635\n",
" 90 60 216.044619962\n",
" 91 60 214.319023132\n",
" 89 61 209.483749377\n",
" 90 61 211.040683789\n",
" 91 61 206.253832044\n",
" 89 62 195.872102692\n",
" 90 62 199.675268885\n",
" 91 62 207.033855917"
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},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img1 = make_4gaussians_image()\n",
"imutils.listpixels(img1, position=(60, 90), shape=(5, 3))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<tr><td>0</td><td>3</td><td>209.483749377</td></tr>\n",
"<tr><td>1</td><td>3</td><td>211.040683789</td></tr>\n",
"<tr><td>2</td><td>3</td><td>206.253832044</td></tr>\n",
"<tr><td>0</td><td>4</td><td>195.872102692</td></tr>\n",
"<tr><td>1</td><td>4</td><td>199.675268885</td></tr>\n",
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"</table>"
],
"text/plain": [
"<Table masked=False length=15>\n",
" x y value \n",
"int64 int64 float64 \n",
"----- ----- -------------\n",
" 0 0 199.200972315\n",
" 1 0 207.01334291\n",
" 2 0 195.240059682\n",
" 0 1 205.135838352\n",
" 1 1 208.919557902\n",
" 2 1 210.512092422\n",
" 0 2 201.415548635\n",
" 1 2 216.044619962\n",
" 2 2 214.319023132\n",
" 0 3 209.483749377\n",
" 1 3 211.040683789\n",
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" 0 4 195.872102692\n",
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"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imutils.listpixels(img1, (60, 90), (5, 3), subarray_indices=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Image cutout"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x108175990>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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fa6z/1L6EZ9ana2at9bGizrGGdWCvZB9E2hqgltMEAACCImgAAEERNACAoAga\nAEBQBA0AICiCBgAQFEEDAAiKoAEABEXQAACCImgAAEERNACAoAgaAEBQF+3EZ6kYIFvyWYfFlRjr\nJd9QzVTPOtfuc8caV1qmj7bb5+jJNtZf41hjr6OnyDi8MsOxhmeC4XFjveeYWAdkSvY72Qf2JQ61\n2Husu+8ZRPl/HT35jh4r6+OEJI3o8a1INspQ2yRpTwp1PKMBAARF0AAAgiJoAABBETQAgKAIGgBA\nUAQNACAoggYAEBRBAwAIiqABAARF0AAAgiJoAABBpdWss88kHTHUFxl/fr2xXpJOOHqsY6UmOdbQ\nKUeP5waIGut3OdYY5Oixzi476Fgj7uixHvwdjjU8+/KJrXy/Y86bZ2bfVmO9Z2SdZ26ZZz7a94z1\nnrltWY4e63Gx/Mqn+vjIMxoAQFAEDQAgKIIGABAUQQMACIqgAQAERdAAAIIiaAAAQRE0AICgCBoA\nQFAEDQAgKIIGABAUQQMACCqthmqOlZRnqL/M+PNbjfWSbXu863jmKv5Dg6PJM13wM2N9wrFGjqNn\nn7Heemfx+l/Geut+SJLj2CeMQzJj9iVc81TPGOsdsz6119FT7OhpNtaPcqxh/XWU7MM7RxtqIynW\n8YwGABAUQQMACIqgAQAERdAAAIIiaAAAQRE0AICgCBoAQFAEDQAgKIIGABAUQQMACIqgAQAEZZp1\ndvLkSb3++uuqqalRbW2tWlpa9Oijj+qWW25Jqt23b58WL16s7du3KzMzU1OmTNGDDz6oYcOG9djG\nAwDSnylompqa9Oqrr2rkyJEqLi7Wxx9/rEgkeaxaXV2d5s+fr+zsbFVWVioWi2nVqlXas2ePli5d\nqoEDu142Jilq2B7rnETr0DtJ+tzRM8FY75n3uP8re8/YvY6FrDdaoWMNz98e1kGchxxreO4w1h7H\nlMStjmOfaV3DvoSaHD3WQZyeQ5Lh6PFoNNZ7hvx6Hiu+a6y3HMeTKdaZgiYvL09vvPGGhg8frk8/\n/VT3339/l3Wvvfaa4vG4li1bpvz8r0cGl5SUaMGCBVq7dq1uv/12y7IAgD7M9B5NZmamhg8fft66\njRs3qqysrCNkJGny5MkqKCjQO++8Y95IAEDf1eMfBqirq1NjY6MmTEh+AamkpEQ7d+7s6SUBAGms\nx4OmoeHrszKNGDEi6boRI0aoublZp0+f7ullAQBpqseDJh6PS5Ki0eS39dsva68BAPR/PR40gwYN\nkiQlEslfBeLEAAAST0lEQVTn9G2/rL0GAND/mT51lor2l8zaX0L7toaGBuXk5HT78ea/KfljmAXy\nfWIWANBzmpT80edUP6Ld40GTn5+v3Nxc1dbWJl1XW1ur4uLibnuvlpTb0xsEALhgw5T8lbeTkvak\n0BtkBM1NN92k9957T3V1dR2Xbdu2TQcOHFB5eXmIJQEAacr8jGb16tVqaWnR0aNHJUmbNm3SkSNH\nJEl33nmnsrKyNGfOHFVXV+uRRx7RrFmzFIvFtHLlShUVFenWW2/t2T0AAKQ1c9CsWrVKhw8fliRF\nIhG9++672rhxoyKRiH70ox8pKytL+fn5eu6557RkyRK99NJLikajuuGGG/TAAw90+/4MAKB/Mj/q\nr1ixIqW68ePH69lnnzVvkIV1TJTn/Z8CR491rpRnnlq2o+dIi70n39gTOWFfQ6McPZ6hT1aeA2Mc\nxuWZWefZLOscrgOONYY4eqzb5flg0EZHT/JHmc7POofNOhPRs4ZkPy6W+1eqd19OEwAACIqgAQAE\nRdAAAIIiaAAAQRE0AICgCBoAQFAEDQAgKIIGABAUQQMACIqgAQAERdAAAIIiaAAAQaXVKOWows5K\nPOPoiTt6HHMS01bMWJ/lmEZ44qi9xzq4dK99CdcAR+ug14sxIFOyD2Osd6xxyNFjHXT7iWONqKPn\n7BN8paLu/CWdWO/DknTK0WM99paBvRkp1vGMBgAQFEEDAAiKoAEABEXQAACCImgAAEERNACAoAga\nAEBQBA0AICiCBgAQFEEDAAiKoAEABEXQAACCSquhmvWSEob6Ucaf7xl2ecLRYx2ul+9YwzrsUvIN\ncPzQWH9dq2MRh5DDV9tVO3qsx8Uz6NUzvDLV4YftPPcvzzGx7r9nqOQQR892R49lGKUkOebPuh4r\nvjDWW4Zwpno8eEYDAAiKoAEABEXQAACCImgAAEERNACAoAgaAEBQBA0AICiCBgAQFEEDAAiKoAEA\nBEXQAACCSqtZZwMlZRrqLTN5JGmksV7yzVbaYayf4FjDcju188x6s7IeE8m3L9a5bTsda3hmd1lH\nvXnmcFlnV0n2ffEcx4OOHquoo8czt806t0yy38afOdbwPIZZez411KY6m5JnNACAoAgaAEBQBA0A\nICiCBgAQFEEDAAiKoAEABEXQAACCImgAAEERNACAoAgaAEBQBA0AICiCBgAQVJChmh9//LF+8Ytf\ndHnd4sWLNXHixC6va5VtKKF14KVnuJ5nqOYYY71neONhR88IR4/1NrMOu5SkDEdPnbHeOuzSs4Zk\nH0a5z7GGZ+Bjo7H+jGMNz3G0/n4dd6xh/X2UfL/31t/j0Y41PANVs4z1lvvXKUlHU6gLOr151qxZ\nmjCh82zi0aM9Ny8AoK8KGjRXXXWVpk6dGnIJAECaC/oeTVtbm2KxmM6c8TwRBwD0B0Gf0Tz77LM6\nefKkBgwYoKuvvlr33Xdf0ktpAID+LUjQZGZmaurUqbrhhhs0bNgw7d27VytXrtT8+fP14osvqri4\nOMSyAIA0FCRoSktLVVpa2vHfZWVlmjp1qn7+859r2bJleuaZZ0IsCwBIQ0FfOvu2MWPG6Ac/+IE2\nbtyotrY2RSKRpJr/6mKDRn3zDwDQe44r+ePlqX5t4KIFjSTl5+fr9OnTOnXqlAYPHpx0/ZWSci7m\nBgEAUpKj5MfnU0rtu2AXdTLAl19+qUGDBnUZMgCA/ilI0DQ2Jn8PedeuXdq0aZOuvfbaEEsCANJU\nkJfOfvWrX2nQoEEqLS1Vbm6u9u3bpzVr1mjw4MGqrKwMsSQAIE0FCZobb7xRVVVV+rd/+zfFYjHl\n5uZq6tSp+tnPfsYIGgC4xAQJmjvvvFN33nmnuS9bUq6hvt748z2vE0YdPdbBkicca3ji+lNHj3Wm\ng2eopnXonyRlGus9sykaHD3WQZyeQad7HT1WniGk1oGikn0QpWdwp/VxQpK+cvRYee6T+Y6ekI9H\niRTrOE0AACAoggYAEBRBAwAIiqABAARF0AAAgiJoAABBETQAgKAIGgBAUAQNACAoggYAEBRBAwAI\n6qKe+Ox89ks6aqgvNP78L4z10tfz10IrcvSkcrKhs3nmo1lnd3lur0OOHutt9qFjDc/sLutt/K5j\nDc+8q+QTd5yb5zgOc/RYZ4p95ljj+44ez1l9rTPFPPPUPDPorDPVLCefPJliHc9oAABBETQAgKAI\nGgBAUAQNACAoggYAEBRBAwAIiqABAARF0AAAgiJoAABBETQAgKAIGgBAUAQNACCotBqqOVBSpqHe\nmpKXGesl32DF8cb6/+VY47uOHg/rwMs8xxq1jh7rgFTPsR/n6LHeXwY51kg4ei7G/cU6gNXTc5Vj\nje2Onu85eiyPXZJ02LGG55lB1Fi/11Cb6pBPntEAAIIiaAAAQRE0AICgCBoAQFAEDQAgKIIGABAU\nQQMACIqgAQAERdAAAIIiaAAAQRE0AICgCBoAQFBpNVQz45t/qWow/nzPgMx8R89nxvpcxxqegXwe\n1n3Jcqzh6fEMybSy7rsk7TTWe4aQevZ9h7H+lGMN61BJj3pHz/cdPSccPaOM9bscaxQ4eqwst1eL\npE9SqOMZDQAgKIIGABAUQQMACIqgAQAERdAAAIIiaAAAQRE0AICgCBoAQFAEDQAgKIIGABAUQQMA\nCCrIrLNEIqE///nP2rBhg1paWlRUVKS5c+dq8uTJ5+w7aVxnvLHeMketXa2j5yZjfaNjDc8crkGO\nnq8C10u+2V3WuVqeGVmtjp4xxvohjjU892PrX5Se7fLMOrPO7PueY40mR4/nd9K6L5Mca5xx9Fjn\n71l+H+Mp1gV5RvPMM8/o3//93zVjxgw99NBDGjBggH75y1/qk09SGb8GAOhPejxoampq9Pbbb6uy\nslL33XefbrvtNv3hD3/QyJEj9cc//rGnlwMApLkeD5rq6mplZGTojjvu6LgsGo3qxz/+sXbs2KG6\nurqeXhIAkMZ6PGh27dqlgoICDR48uNPlEyZM6LgeAHDp6PGgqa+vV15e8umc2i+rr/e8LQsA6Kt6\nPGgSiYQyM5M/fxKNRiVJ8Xiqn1MAAPQHPf7x5mg0qq++Sv6QayKRkCQNGtT9h2y/VPJHN4d987+e\n0x33B19IGt3bG9FLDsr+ceH+4rCkkb29Eb0kJt/Hq/uDI5L+obc3ohsnlHyK61S/AtDjQZOXl6ej\nR48mXd7+kllXL6u1+0dJg7u4fJ8u3aD5Updu0HwhguZSdFIETTrK+ubft8UlHUqht8dfOisuLtaB\nAwcUi8U6XV5TU9NxPQDg0tHjQVNeXq7W1latWbOm47JEIqG1a9dq0qRJys/P7+klAQBprMdfOps4\ncaLKy8u1bNkyHTt2TKNHj9a6det05MgRPfbYYz29HAAgzQWZdfb444/rT3/6kzZs2KDm5mZ95zvf\n0W9+8xtdddVVXda3f1Cgu8+jnVHXc9CsM4zOfiMrFQlHT4Oxvvkc132lrvfzlHENSTru6LGuc8yx\nRne3cXf73n6dhWffPcfe2tPd7KrT6v5+4XkZwjpH0MMz566r26utm8sl33H0/N57fr9OG+u7Or7n\nOu6Sb/6e9bhEHD+7/TG825/59ttvtxm3o8dVVVXp17/+dW9vBgDA4cknn9T06dO7vT4tgqaxsVEf\nfPCBRo0a1fF9GwBAekskEjp06JCuvfZa5eZ2/9ngtAgaAED/xYnPAABBETQAgKAIGgBAUAQNACAo\nggYAEFSQL2z2hEQioT//+c/asGGDWlpaVFRUpLlz52ry5Mm9vWlBffzxx/rFL37R5XWLFy/WxIkT\nL/IWhXHy5Em9/vrrqqmpUW1trVpaWvToo4/qlltuSardt2+fFi9erO3btyszM1NTpkzRgw8+qGHD\nhnXxk9Nfqvv+9NNPa/369Un9hYWFWr58+cXa3B5VW1urdevW6aOPPtLhw4c1bNgwTZw4UXPnzlVB\nQUGn2v523FPd9/543NM2aJ555hn99a9/1V133aWCggK99dZb+uUvf6k//OEP3U4Y6E9mzZrVcVbS\ndqNH9585zk1NTXr11Vc1cuRIFRcX6+OPP1Ykkvyd5Lq6Os2fP1/Z2dmqrKxULBbTqlWrtGfPHi1d\nulQDB6btXbhbqe67JGVmZmrBggWdLsvKOnuGbt+xYsUK7dixQ+Xl5SoqKlJDQ4NWr16tefPmafHi\nxbriiisk9c/jnuq+S/3vuKfl0aqpqdHbb7+tf/mXf9Hs2bMlSTNmzNC9996rP/7xj3rxxRd7eQvD\nu+qqqzR16tTe3oxg8vLy9MYbb2j48OH69NNPdf/993dZ99prrykej2vZsmUdA1lLSkq0YMECrV27\nVrfffvvF3Owekeq+S9LAgQPP+Y3rvmb27NkqKSlRRsbfzzw1bdo03XvvvVqxYoWeeOIJSf3zuKe6\n71L/O+5p+R5NdXW1MjIydMcdd3RcFo1G9eMf/1g7duxQXV1dL27dxdHW1qZYLKYzZ7qbhtW3ZWZm\navjw4eet27hxo8rKyjpN/Z48ebIKCgr0zjvvBNzCcFLdd+nr+0Fra6tOnPBM7Eo/paWlnR5oJWnM\nmDEaN26c9u/f33FZfzzuqe671P+Oe1o+o9m1a5cKCgo0eHDn06C1v5S0a9eufn+6gWeffVYnT57U\ngAEDdPXVV+u+++5Leimtv6urq1NjY2OX+11SUqL333+/F7bq4orH47rtttsUj8eVnZ2tiooKzZs3\nL+l3oy9ra2vTsWPHVFRUJOnSOu5n73u7/nbc0zJo6uvruzwTZ/tl7Wfr7I8yMzM1depU3XDDDRo2\nbJj27t2rlStXav78+XrxxRcvqRPHNTR8PQd7xIgRSdeNGDFCzc3NOn36dJ98vT4VeXl5uueee3Tl\nlVeqtbVVW7Zs0Ztvvqndu3dr0aJFSX8d91VVVVWqr6/X3LlzJV1ax/3sfZf653FPyyOVSCSUmZmZ\ndHn7wM14vLsTCvR9paWlKi0t7fjvsrIyTZ06VT//+c+1bNkyPfPMM724dRdX+3HuatDqt+8L/eEB\npyuVlZWd/nvatGkqKCjQyy+/rOrqalVUVPTSlvWc/fv36/nnn1dpaalmzpwp6dI57l3tu9Q/j3ta\nvkcTjUb11VfJZ1FoP+fBoEGDLvYm9aoxY8boBz/4gT766CO1tV06M1Dbj3NX57q4VO8Ld999tyKR\niD788MPe3pQL1tDQoMcff1xDhw7VwoULOz55dykc9+72vTt9/bin5Z8EeXl5Onr0aNLl7S+ZdfWy\nWn+Xn5+v06dP69SpU332dVqr9pdO2l9K+baGhgbl5OT0+b9qraLRqHJyctTcfK7TY6W/lpYWPfbY\nYzpx4oReeOGFTi+T9ffjfq59705fP+5p+YymuLhYBw4cUCwW63R5TU1Nx/WXmi+//FKDBg26ZEJG\n+jpcc3NzVVtbm3RdbW3tJXk/iMViampqOue5P9JdIpHQk08+qYMHD+o3v/mNxo4d2+n6/nzcz7fv\n3enrxz0tg6a8vFytra1as2ZNx2WJREJr167VpEmT+vUnzhobG5Mu27VrlzZt2qRrr722F7aod910\n00167733On2kfdu2bTpw4IDKy8t7ccvCSiQSSX9oSdIrr7wiSbruuusu9ib1iDNnzmjhwoWqqanR\nU089pUmTJnVZ1x+Peyr73l+Pe1o+/5w4caLKy8u1bNkyHTt2TKNHj9a6det05MgRPfbYY729eUH9\n6le/0qBBg1RaWqrc3Fzt27dPa9as0eDBg5PeJOzrVq9erZaWlo6XSTdt2qQjR45Iku68805lZWVp\nzpw5qq6u1iOPPKJZs2YpFotp5cqVKioq0q233tqbm39Bzrfvzc3Nqqys1M0336zCwkJJ0tatW7Vl\nyxZdf/31uvHGG3tt2y/E0qVLtXnzZpWVlampqUkbNmzodP2MGTMkqV8e91T2vaGhoV8e97Q9w2Yi\nkdCf/vQnVVVVqbm5Wd/5znd077339vu/6v/jP/5DVVVVOnjwoGKxmHJzc3XNNdfoZz/7Wb8aQSNJ\n//zP/6zDhw9LUseboW1tbYpEIvrXf/1XjRw5UpK0d+9eLVmyRJ988omi0aimTJmiBx54oM++jCCd\nf9+zsrL0wgsvqKamRkePHlVra6sKCgo0ffp0zZ49u09+xFWSHnnkEf3tb3/r8kMtkUhE//mf/9nx\n3/3tuKey7y0tLf3yuKdt0AAA+oe0fI8GANB/EDQAgKAIGgBAUAQNACAoggYAEBRBAwAIiqABAARF\n0AAAgiJoAABBETQAgKAIGgBAUAQNACCo/w+ejTcYmNH2pAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x103a57d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img1 = make_4gaussians_image()\n",
"cutout = imutils.Cutout(img1, position=(60, 90), shape=(38, 29))\n",
"plt.imshow(cutout.data, cmap='hot')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[41 76]\n",
"(19.0, 14.5)\n",
"(60.0, 52.5)\n",
"(slice(41, 79, None), slice(76, 105, None))\n",
"(slice(0, 38, None), slice(0, 29, None))\n",
"(41, 76, 79, 105)\n",
"(0, 0, 38, 29)\n"
]
}
],
"source": [
"print(cutout.origin)\n",
"print(cutout.center_small)\n",
"print(cutout.center_large)\n",
"print(cutout.slices_large)\n",
"print(cutout.slices_small)\n",
"print(cutout.bbox_large)\n",
"print(cutout.bbox_small)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Image centroid"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from photutils.morphology import centroid_com, centroid_1dg, centroid_2dg"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(13.987236187316199, 18.934136422458938)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x, y = centroid_com(cutout.data)\n",
"x, y"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(14.015495940094592, 18.995010160332413)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x, y = centroid_1dg(cutout.data)\n",
"x, y"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(14.002854040002291, 18.998669790449672)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x, y = centroid_2dg(cutout.data)\n",
"x, y"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(89.987236187316199, 59.934136422458934)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# centroid relative to original image\n",
"x, y = centroid_com(cutout.data) + cutout.origin[::-1]\n",
"x, y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Image moments"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from photutils.morphology import data_properties\n",
"props = data_properties(cutout.data)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5.45847224e+04, 1.03310160e+06, 2.26475376e+07,\n",
" 5.45063739e+08],\n",
" [ 7.63706333e+05, 1.52120754e+07, 3.45705238e+08,\n",
" 8.54635815e+09],\n",
" [ 1.25303178e+07, 2.58432209e+08, 6.03968075e+09,\n",
" 1.52710826e+11],\n",
" [ 2.26749122e+08, 4.79798718e+09, 1.14651053e+11,\n",
" 2.95269183e+12]])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# spatial moments up to 3rd order\n",
"props.moments"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(54584.722369171919,\n",
" 1033101.6003429986,\n",
" 763706.33278660406,\n",
" 15212075.445035588)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first-order moments\n",
"props.moments[0, 0], props.moments[0, 1], props.moments[1, 0], props.moments[1, 1]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5.45847224e+04, 7.14010184e-10, 3.09446727e+06,\n",
" -7.11785915e+05],\n",
" [ 8.72631745e-10, 7.57733637e+05, 1.56176683e+05,\n",
" 9.70942135e+07],\n",
" [ 1.84514189e+06, 7.30421693e+04, 1.35634163e+08,\n",
" -2.79980424e+07],\n",
" [ -1.96727562e+05, 5.83499846e+07, -2.89012439e+06,\n",
" 7.76721078e+09]])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# central moments (translational invariant) up to 3rd order\n",
"props.moments_central"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<Quantity 0.03289033279698604 1 / pix2>,\n",
" <Quantity -0.016107529583733286 1 / pix2>,\n",
" <Quantity 0.01961156013719497 1 / pix2>)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# SExtractor ellipse parameters\n",
"props.cxx, props.cxy, props.cyy"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2.59088842e+02, 4.18861196e+03, 1.17563789e+05,\n",
" 3.71994172e+06],\n",
" [ 3.99273858e+03, 4.79426804e+04, 1.11606326e+06,\n",
" 3.30672947e+07],\n",
" [ 8.97640714e+04, 9.96343129e+05, 2.05463332e+07,\n",
" 5.65562989e+08],\n",
" [ 2.21121205e+06, 2.46663381e+07, 4.85167687e+08,\n",
" 1.28115069e+10]])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# update or make a mask from data lower and upper bounds\n",
"ndout = imutils.mask_databounds(NDData(cutout.data), lower_bound=0, upper_bound=5)\n",
"props = data_properties(ndout.data, mask=ndout.mask)\n",
"props.moments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Masked pixel interpolation"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from photutils.utils import interpolate_masked_data, mask_to_mirrored_num"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x108858550>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zwZJiDPW3GH/+cWO9yxqSfaZYo8Makxx6ouwt1plPP7Av4dRjnHWW9zf/x7xEruyzzv6T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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1012f4d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = np.copy(cutout.data)\n",
"img[10, 8] = 250.\n",
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x10893b190>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4WPn59rP3AQB6piv2PZr77rtPgUBAu3btulJLAgAigLf3aC4WExOjhIQE1dTU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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108892550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mask = (img > 240)\n",
"result = interpolate_masked_data(img, mask)\n",
"img2 = result[0]\n",
"plt.imshow(img2)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x108b76150>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7f1l7MgyPX2eSpbLOy3hGAwDwiqABAHhF0AAAvCJoAABeETQAAK8IGgCAVwQNAMArggYA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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108892b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img3 = mask_to_mirrored_num(img, mask, cutout.center_small[::-1])\n",
"plt.imshow(img3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Radial distance image"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x108db0210>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+Szc6lgY7jfbTZhAyHCQsOoBot5r2tc+OrpSY4bPXF6rMUjUyHMhMtruWJqHl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\nGAAAQBAbBgAAEMSGAQAABLFhAAAAQf8/MTO8WHUAF/QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108c54bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = imutils.radial_distance(shape=(100, 100), position=(45, 37))\n",
"plt.imshow(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Block reduce and block replicate"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(100, 200)\n",
"(50, 66)\n"
]
}
],
"source": [
"img2, err, wcs = imutils.block_reduce(img1, (2, 3))\n",
"print(img1.shape)\n",
"print(img2.shape)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x108e5ff50>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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c+/yuqrLbJMbfVzX6BLPRaDQajUajsavoH5iNRqPRaDQajV3FHqTIPxebHIGR\ne3azojkjRmpISqui0DwO9rj7s8U9k+I9ESPVYpR2lTi7ii6XDvNIPEfgVhG/3ictIXUhN7NatGc/\njJ6PGHVidGc17ip5fD5238J20cZCakG6TzrMubd/VQRupqylAqRUquTSUhdSujcgS8cYhRwxjl29\nmWBbnVeUjzpTB44vR1ZXbhuOQ31oU64Z27B/UmMRYzSw9qbLguOukhyfW8gij3UFWXpRFwntXkpw\nglwlh7Z/OfL+w9v8bQuuUW1iilxR59m9yDlzr7GN9yJXCbKrfbRyZ4oYaUvt1jFJTbuOXVdyyOq2\ncuWIqG1BaF+679gn9jJdZtZTVLcJ1R3q3chryEeKLkmRq/Ltvtb7aNto9mFdrhTXJ3PxAN+V4w4i\nR5FrI3+4+F3DvqGtVtlR/B7kBOB+71SEe96ZxT2ujSnySnE9uzO5d7iHaXvuZb63ova9nrO0VBlD\nqiIEVeaMoqDAsPbyWNWz+1QuXHOi6MuIb+sH5u233x7/6l/9q7j99tvj/vvvjwsvvDBe+MIXxite\n8Yo444wzTt531113xZvf/OY4cuRInHbaaXHZZZfFtddeG+edd96301yj0Wg0Go1G47sQO/6B+bnP\nfS7+0T/6R3HgwIG45ppr4txzz40jR47E2972tvjMZz4T//yf//OIiDh27Fi84Q1viHPOOSde97rX\nxQMPPBA33HBD3HnnnXHdddfF0tIePDRtNBqNRqPRaOwadvxr7/3vf3/MZrP4tV/7tXjiE58YEREv\neclL4uGHH44//dM/jfX19di3b19cf/31ceLEiXjrW98aF1xwQUREHD58ON74xjfGjTfeGFdfffVf\no2se73IEvD9FS65J1Uhved8qsglOp8geRUspFbRCRIyciPAY3ePuqjZuVSs4H2VLz0gpVjXYpcmk\nh41mM8GyUYaT1HZVx7ZK1F7R6F6v6gbn9j5a3FdR4UL7cF7td9bzpOiHuq2oP++RLtK2pVYiRr05\npooirCKlq2Te9jW7m0jXT5CnyOpAaqeibxxPpr2cP/Wu3tSVUbBVVLFzXyVgj6hpIvaXJSjkme+y\nfxcX19WN9HPEODf+7UXI2qr9U/9VzXcTS0eMtL8Umu9yHO5B2peRqNWayVGpVV1mbVJa3Hmt7ENk\nirxKlq6etZEqitn3PrD4+oHk3jBlDvYxNya0lyI3QlxaXJOv7o8YI9Idhu/aqFy/imwjx7lnmXs2\ncrS+kdJs5cpwAAAgAElEQVTSxq6BG5G1HdvWDhxEdtPQncM9c4LsGnD+qkIF/kYwY0t2p6mSubuO\nXWPuc1W9c/u0mtqr1oC6Qj/LuIptVFkmXK/OUV5X2ohjMrPL4yPijNgJdhzks0WB79+/f7h+/vnn\nxyMf+cg47bTNCf3gBz8Yl19++ckflxERz3nOc+LQoUOxurq60+YajUaj0Wg0Gt+l2PEPzKuuuioe\n/ehHx7/4F/8i7rjjjvjyl78c73//++Nd73pXvPzlL48zzjgjjh07Fmtra3HJJZec8vzhw4fjs5/N\n/3fbaDQajUaj0fhew44p8gMHDsRv//Zvxy/+4i/G61//+pPXX/WqV8VrXvOaiIi4557NY9jzz8+1\ndzev3XfffTGbzb6FH+YZsUlVVHWmwVr+wepx8iuQ3478WuSdRCHnBOdbyIlG7aNJeh2H1NMEuaBg\nhvszbazbgHS2fZdmqKj3dyNLdUiBPDG17dhto4qA9p4qQt9x57Had/tSuTL43oomsL1T7XXx89Ir\njql6r3MkZVpRD7mNFWT1VkUjSu2oA/thUnHnKGJcA9JYjk+q2SwF0o5VzfftsgNU7iBVnfCKeq1o\nsu0iiqXcpMWNKlY36HCI+EUHQ9367LJhX2xD/UyQq6TrVcR1TsBupLR2pG5tr0pYrR1U2S5ydgZp\nf+emKrJQJazWxUQ9ZYrcfaCiAnUt0BXBMblmCvs/nt2iiswe1iIf6r8X1yUJ/VT6nohxOVTPfBz5\nAHo+St+HyHH0NCTTz8nAqywmNi4t7nxLLTsvFZUdMX5T/R5UhUVcY6vIK8iuy4p2z89rh+pAW/W3\nwAPFdd19cnYN16jPF9lRqnnSTWPddVJlPYkoafhTfpdMYyfY8Q/Me+65J37xF38xIiL+yT/5J3He\neefFX/zFX8Tb3/72ePSjHx0ve9nL4sSJzdD1008//ZTnt66dOHGiA30ajUaj0Wg0voex4196v//7\nvx/Hjh2L3//9348DBw5ERMQVV1wR3/jGN+Itb3lLvPCFLzzpp/nggw+e8vzWNdMZNRqNRqPRaDS+\n97DjH5if/OQn4ylPecrJH5db+OEf/uH4d//u38Udd9wRT3jCEyJiTpWLe+65J84999wdnF7+ekSc\nF+PR7esj4u9+U/Y4eJKelUryqFfKx8gxKRGPy6eFLHJUXUWzVvRiFRktZSCtmhOAV9HK6pcEwUvS\nd1VSWHS7TFTdRqaCpDjsuxRApQ/npaobnaP4pE5MgKyN6MogZZfr1W/BeXE8L97mPvtR1YHONPCi\n69bdvjjdN0V2Xm1balu6wzYcd25j0TsztMMqUtp7mPsDXD/uO1dTGy9Hds6qCFDvubi4vpNnI+po\nbKi4Q9jaUale9V9lLKgSLGdoC+h2P2tjzb5qq9rmFNmI2IhxPZnk3+ftr/RkFV2urUlTqqeIOgl7\nUbN62CN1JRHO8XZuF1XWj8q1wDXjXqbOna9ME7MnrKPPdfa2A+zp0+JVVT2BTJG7Lfv84eIeo9CP\nSourQ8awRJT8LOvZvde9sHLzcA2Y9aRyYcp7k/OqjTjHPl8VSHH/+nRxPX+7bE9bnSLrpmbb/t7w\nPU6sbjIRdaYH5dWibfS8rluBbm3qLLvD2MYW3f6vY3Qz/GqkbP4ldvwDczabxTe+8Y2F1yMiHnro\nobjgggti//79cdttOU1GxG233RYXX1x96MSvR8SzYpy8py2+tdFoNBqNRqPx3wl/NyJ+in//ZWym\nQnvVt3xyx1HkT33qU+Mzn/lMHD06nma9//3vj0c+8pHx5Cc/OSIinv/858fNN98cx44dO3nPrbfe\nGkePHo0rr7wyGo1Go9FoNBrf29jxCeYrXvGK+MAHPhA///M/Hy972cvinHPOiZtvvjk+8pGPxEte\n8pKTkeOvetWr4qabbopf+IVfiGuuuSYeeOCBeMc73hFPetKT4qqrMnWzCMci4gsRS5xazqpEvB61\nR4xRT1WEpJSIyXdVhQlRpamrhOERA8W0BGU0MxlvRc3YP4/HryyuR9R1ows6c+axuAm1HZMRhPY1\n08xSVznCfAvSWFJo0vPSchPkXPdWesW5caxG4v0BsvMkzVK5FeSsAYxjH+NYtx8+r71UbgzSQpmO\n0abtr1GK6sP5qygm14LtZZuyX85flcjfJMLM5XEzE0jf5GhJUSUIrurKV0ULhLTQV9Lf2CsOFAm9\nrSFdUXRVhLDrPu+y67kvW0Dna64NGRxpL23b9ZpcTJZ/Zi5vSO+7fuRYJ8iVS5F7Le3lsc6MPnY9\n/D/I0varyNqwz7pGM8XnGnDOfL6w27JIhbLrMIPv2xJta7a6jGgvkn7PRda+pL4jRpZSzzWpcBMI\n3FK0cYd2hx3kbXhAlQHCvUa9TZE1kirpes4OICXvXlHNmXuQNmIxESPC3c9zdprKzU03CtvW5idz\ncT/fhrXtMsS4ttSte321j1d7pL8ldPPIY62KZ7h+DsWp2SIWY8c/MJ/85CfHb/7mb8bb3va2eMc7\n3hEPPvhgPO5xj4vXvva18Xf+zt85ed8FF1wQb3rTm+J3fud34i1veUucfvrp8bznPS9+7ud+rqPH\nG41Go9FoNP4G4Nv6xff93//98eu//uvf8r7JZBK/8Ru/8dfuVKPRaDQajUbjuxd78EjxCRFxccRM\nKshj6Sly7r5HulLQRmlJEVUJnT229xi8imqMGI6MZx63T+biUNvYfnhcbmStx/m5NutOouSqiO23\nFe1JhdqnVPN9oKis517VJ66SGXu0b78zF1RxNbYnjV8lnXZ82pR2I6cUMdAJ64xjH22sS8k77or+\nkcLM86q9ad86VGvP6g0dLFfuDrY9SW1Xc6aubM/3ao8ryNLBORuBtqB9So9J2UyRKx36Hp41sj1i\njJyd6QbBPVKNUpBr3C/lKZ15KfItiYbShWZIpO2+kdfcFqTLtVvdD1J7G+6l7mFV3e4zi3t2sGaG\nfS0i9kPjrzk+11kVqb6TRPD5GyA96Tik+LRtXZgsHKANVtk/Mr3I87MqoNXk144VPUl9u9XmKPIq\nWnyK7LtWkJcKWTs3SfvRTFmztkzyvua4nYsqIXqVmSC7DrkPaEeV25PfNefp64VcJX+PGNeciq7W\njK53YM1vgOs7u8xUxVYqSl5dVQVHTOzu/Tkbiu3pQuC+MY1TjXExdhzk02g0Go1Go9Fo7AT9A7PR\naDQajUajsavoH5iNRqPRaDQajV3FHvTBvD82fcn0e9CHYjtfCX0O9AXSf0A/hknRhyoty11FWxGj\nP6DP0PeZ6YH0dTCVS5UuKacD0m+jSk+j34o6NGWB7enLZuqQaWr7Z5D1havSilRpdc5ffH05JdYf\n3D2cP1Oo2PfKrPUpUf/eP03PFFVY1isfYX1Bfe8EWT+l7F9q6hn7qN70v1HP6G1D+6h8HbNPZOE7\nPMiVX5z98L3qI1fnqHwtWbv79Flz7vUdMsWOabAYt1VJIiIOmZqF61VKmRXkf4v8dGRVo+/bUvI/\ns41Z5ZfF9WXmbPCn1TZd31nPnyz+NkVW/+6Xriv1XKUASj7Fa+7DrpPVWIwJsuObIrvnuKYjTq3Y\ntOi9Pq9dqBv3VO1Ov8K8N7i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U+Q7ri/UKZOfCtrGpCXY7nUSNKbLzXaXEcI4cK2tB36n8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\nzrFZNxxrTrTuXqGvhmv8FcW7pGinxTuz64P3OSazFFQpFnyX4/OD4Lc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tc5zNminWOueC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\nNQV2iYM7UMaFi6OuS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NCQ7rnnHh111FFFmI9EolNs2LBhzLVVq1bpJz/5iY4//viRayee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\nKv4/m6pCa5cibFEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10875ee10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img1)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1093f62d0>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SKRNMSZIklTLBlCRJUqkdroNZb2j6LgRKi/XSQrwEKVTbCmLomshloQVvW0EMKQZLCw3T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\na1rsJ/2MaQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10889b250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# note the aspect ratio change\n",
"plt.imshow(img2)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50, 66)\n",
"(100, 198)\n",
"(175277.23119258526, 175277.23119258526)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x109c1aa50>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XUrIelcxaJ2sWzaYn1yEZBzpWJI7eM0lb5B72Xni8pwsdL2Hzj2Z+k3lDzjOd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\nJKkoN5iSJEkq6v8H2h2YIRGyxyEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109919890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img3 = imutils.block_replicate(img2, (2, 3))\n",
"print(img2.shape)\n",
"print(img3.shape)\n",
"print(img2.sum(), img3.sum())\n",
"plt.imshow(img3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# imarith"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3.9764617025764353, 8.9764617025764348)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img1 = make_4gaussians_image()\n",
"nd1 = NDData(img1)\n",
"nd2 = imutils.imarith(nd1, 5, '+')\n",
"nd1.data[0, 0], nd2.data[0, 0]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.44298765307884136"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nd3 = imutils.imarith(nd1, nd2, '/')\n",
"nd3.data[0, 0]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1500"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the operand can also be applied to meta (header) keywords\n",
"nd1.meta['exptime'] = 500\n",
"nd2.meta['exptime'] = 1000\n",
"nd3 = imutils.imarith(nd1, nd2, '+', keywords='exptime')\n",
"nd3.meta['exptime']"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0 0 6 12 0]\n",
"[ True True False False True]\n"
]
}
],
"source": [
"# NDData.mask is used in arithmetic\n",
"img1 = np.arange(5)\n",
"img2 = np.arange(5)**2\n",
"mask1 = img1 > 3\n",
"mask2 = img2 < 2\n",
"nd1 = NDData(img1, mask=mask1)\n",
"nd2 = NDData(img2, mask=mask2)\n",
"\n",
"nd3 = imutils.imarith(nd1, nd2, '+')\n",
"print(nd3.data)\n",
"print(nd3.mask)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0 2 6 12 20]\n",
"[ 0. 1.41421356 2.44948974 3.46410162 4.47213595]\n"
]
}
],
"source": [
"# NDData uncertainties are propagated for +, -, *, /\n",
"err1 = imutils.StdUncertainty(np.sqrt(abs(img1)))\n",
"err2 = imutils.StdUncertainty(np.sqrt(abs(img2)))\n",
"nd1 = NDData(img1, uncertainty=err1)\n",
"nd2 = NDData(img2, uncertainty=err2)\n",
"\n",
"nd3 = imutils.imarith(nd1, nd2, '+')\n",
"print(nd3.data)\n",
"print(nd3.uncertainty.value)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:astropy:Error propagation is not performed for the '//', 'min', and 'max' operators.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Error propagation is not performed for the '//', 'min', and 'max' operators. [imutils.core]\n"
]
},
{
"data": {
"text/plain": [
"array([0, 1, 0, 0, 0])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nd3 = imutils.imarith(nd1, nd2, '//')\n",
"nd3.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Command-line scripts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# fitsinfo"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"usage: fitsinfo [-h] filename [filename ...]\r\n",
"\r\n",
"Print a summary of the HDUs in a FITS file(s).\r\n",
"\r\n",
"positional arguments:\r\n",
" filename Path to one or more FITS files. Wildcards are supported.\r\n",
"\r\n",
"optional arguments:\r\n",
" -h, --help show this help message and exit\r\n"
]
}
],
"source": [
"!fitsinfo -h"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filename: ibug0pgcq_flt.fits\r\n",
"No. Name Type Cards Dimensions Format\r\n",
"0 PRIMARY PrimaryHDU 251 () \r\n",
"1 SCI ImageHDU 142 (1014, 1014) float32 \r\n",
"2 ERR ImageHDU 80 (1014, 1014) float32 \r\n",
"3 DQ ImageHDU 72 (1014, 1014) int16 \r\n",
"4 SAMP ImageHDU 66 (1014, 1014) int16 \r\n",
"5 TIME ImageHDU 66 (1014, 1014) float32 \r\n",
"6 WCSCORR BinTableHDU 59 7R x 24C [40A, I, 1A, 24A, 24A, 24A, 24A, D, D, D, D, D, D, D, D, 24A, 24A, D, D, D, D, J, 40A, 128A] \r\n"
]
}
],
"source": [
"!fitsinfo ibug0pgcq_flt.fits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# imstats"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"usage: imstats [-h] [-e exten] [-s sigma] [-i iters] [-c columns] [-l lower]\r\n",
" [-u upper] [-m mask_value]\r\n",
" [fits_filename [fits_filename ...]]\r\n",
"\r\n",
"Calculate image statistics.\r\n",
"\r\n",
"positional arguments:\r\n",
" fits_filename FITS filename(s)\r\n",
"\r\n",
"optional arguments:\r\n",
" -h, --help show this help message and exit\r\n",
" -e exten, --exten exten\r\n",
" -s sigma, --sigma sigma\r\n",
" The number of standard deviations to use as the\r\n",
" clipping limit\r\n",
" -i iters, --iters iters\r\n",
" -c columns, --columns columns\r\n",
" -l lower, --lower lower\r\n",
" -u upper, --upper upper\r\n",
" -m mask_value, --mask_value mask_value\r\n"
]
}
],
"source": [
"!imstats -h"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0;31m filename npixels mean std min max \u001b[0m\r\n",
"\u001b[0;31m------------------ ------- -------- ------ -------- -------\u001b[0m\r\n",
"ibug0pgcq_flt.fits 1027865 0.438514 0.6974 -59.8228 65.8734\r\n"
]
}
],
"source": [
"!imstats ibug0pgcq_flt.fits -e 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# imarith"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"usage: imarith [-h] [-e1 exten1] [-e2 exten2] [-f fill_value] [-k keywords]\r\n",
" [-o outfilename] [-c]\r\n",
" fits_filename fits_filename operator\r\n",
"\r\n",
"Perform basic arithmetic on two FITS files.\r\n",
"\r\n",
"positional arguments:\r\n",
" fits_filename FITS filename (or scalar value)\r\n",
" operator Arithmetic operator. Must be one of '+', '-', '*',\r\n",
" '/', '//', 'min', or 'max'\r\n",
"\r\n",
"optional arguments:\r\n",
" -h, --help show this help message and exit\r\n",
" -e1 exten1, --exten1 exten1\r\n",
" -e2 exten2, --exten2 exten2\r\n",
" -f fill_value, --fill_value fill_value\r\n",
" -k keywords, --keywords keywords\r\n",
" -o outfilename, --outfilename outfilename\r\n",
" -c, --clobber\r\n"
]
}
],
"source": [
"!imarith -h"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0;32mINFO\u001b[0m: Wrote sum.fits [imutils.nddata_adapters]\r\n"
]
}
],
"source": [
"!imarith ibug0pgcq_flt.fits ibug0pgcq_flt.fits '+' -e1 1 -e2 1 -o 'sum.fits'"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ibug0pgcq_flt.fits imutils_demo.ipynb sum.fits\r\n"
]
}
],
"source": [
"!ls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# To install imutils:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pip install git+https://github.com/spacetelescope/imutils.git"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Docs: http://imutils.readthedocs.org/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Github repo: https://github.com/spacetelescope/imutils"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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