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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from astropy import constants, units as u, table, stats, coordinates, wcs, log, coordinates as coord, convolution, modeling; from astropy.io import fits, ascii" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### First, search the ALMA archive for all observations of stars that have known exoplanets" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 164, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import requests\n", | |
"from bs4 import BeautifulSoup\n", | |
"result = requests.get('https://en.wikipedia.org/wiki/List_of_nearest_exoplanets')\n", | |
"page = result.text\n", | |
"soup = BeautifulSoup(page)\n", | |
"tables = soup.findAll('table')\n", | |
"tb = tables[1]\n", | |
"names = [x.text.rstrip(\" \\n#ABC\").replace(\"\\xa0\",\" \") for x in tb.findAll('th')[16:]]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def try_coord(x):\n", | |
" try:\n", | |
" return coordinates.SkyCoord.from_name(x)\n", | |
" except:\n", | |
" return\n", | |
"coords = [try_coord(x) for x in names]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"almahits = {name: Alma.query_region(crd, radius=1*u.arcmin, public=False, science=False) for name, crd in zip(names, coords) if crd is not None}\n", | |
"{x:len(almahits[x]) for x in almahits if len(almahits[x]) > 0}" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 131, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Do the same thing for closest stars" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 139, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"result = requests.get('https://en.wikipedia.org/wiki/List_of_nearest_stars_and_brown_dwarfs')\n", | |
"page = result.text\n", | |
"soup = BeautifulSoup(page)\n", | |
"tables = soup.findAll('table')\n", | |
"tb = tables[1]\n", | |
"names = [x.text.rstrip(\" \\n#ABC\").replace(\"\\xa0\",\" \") for x in tb.findAll('tr')]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"radec_stars = [(x.findAll('td')[5].text.replace('\\xa0','').strip(),\n", | |
" x.findAll('td')[6].text.replace('\\xa0','').strip())\n", | |
" for x in tb.findAll('tr')[4:] if len(x.findAll('td'))>=7]\n", | |
"radec_stars" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"I gave up on that project for now; the wikipedia table is too hard to parse." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Some basic math: How far away can ALMA reasonably detect a Jupiter radius object at 300, 1000, 2000 K?" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"https://almascience.eso.org/proposing/sensitivity-calculator" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 120, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Frequency:660 Time:600, sensitivity: 0.0004592382593455165\n", | |
"Frequency:410 Time:600, sensitivity: 9.419171613474063e-05\n", | |
"Frequency:345 Time:600, sensitivity: 6.1018028041566896e-05\n", | |
"Frequency:230 Time:600, sensitivity: 4.0581114071865645e-05\n", | |
"Frequency:100 Time:600, sensitivity: 3.4604609776195e-05\n" | |
] | |
} | |
], | |
"source": [ | |
"url = \"https://asa.alma.cl/SensitivityCalculator/webapi/sensitivity\"\n", | |
"data = {\"beamsizeInArcsecs\":0,\n", | |
" \"integrationTimeInSec\":600,\n", | |
" \"observingFrequencyInGHZ\":345,\n", | |
" \"bandwidth\":7.5,\n", | |
" \"dec\":0,\n", | |
" \"numberOfAntennas\":\"43\",\n", | |
" \"antennaArray\":\"ARRAY_12M\",\n", | |
" \"polarization\":\"DUAL\",\n", | |
" \"wvIndex\":\"2\",\n", | |
" \"receiverBand\":\"ALMA_RB_07\"}\n", | |
"sens_of_freq = {}\n", | |
"freqs = (660, 410, 345, 230, 100)\n", | |
"for freq in freqs:\n", | |
" data['observingFrequencyInGHZ'] = freq\n", | |
" result = requests.post(url, json=data)\n", | |
" sens = result.json()\n", | |
" sens_of_freq[freq] = sens['sensitivityInJ']\n", | |
" print(f\"Frequency:{data['observingFrequencyInGHZ']} Time:{data['integrationTimeInSec']}, sensitivity: {sens['sensitivityInJ']}\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 121, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Temperature = 300.0 K\n", | |
"D=2.0 pc 0.0160 0.0063 0.0045 0.0020 0.0004 mJy S/N= 0.03 0.07 0.07 0.05 0.01\n", | |
"D=3.0 pc 0.0071 0.0028 0.0020 0.0009 0.0002 mJy S/N= 0.02 0.03 0.03 0.02 0.005\n", | |
"D=4.0 pc 0.0040 0.0016 0.0011 0.0005 0.0001 mJy S/N= 0.009 0.02 0.02 0.01 0.003\n", | |
"D=5.0 pc 0.0026 0.0010 0.0007 0.0003 0.0001 mJy S/N= 0.006 0.01 0.01 0.008 0.002\n", | |
"Temperature = 1000.0 K\n", | |
"D=2.0 pc 0.0555 0.0216 0.0153 0.0068 0.0013 mJy S/N= 0.1 0.2 0.3 0.2 0.04\n", | |
"D=3.0 pc 0.0247 0.0096 0.0068 0.0030 0.0006 mJy S/N= 0.05 0.1 0.1 0.07 0.02\n", | |
"D=4.0 pc 0.0139 0.0054 0.0038 0.0017 0.0003 mJy S/N= 0.03 0.06 0.06 0.04 0.009\n", | |
"D=5.0 pc 0.0089 0.0034 0.0024 0.0011 0.0002 mJy S/N= 0.02 0.04 0.04 0.03 0.006\n", | |
"Temperature = 2000.0 K\n", | |
"D=2.0 pc 0.1120 0.0433 0.0307 0.0137 0.0026 mJy S/N= 0.2 0.5 0.5 0.3 0.07\n", | |
"D=3.0 pc 0.0498 0.0193 0.0136 0.0061 0.0012 mJy S/N= 0.1 0.2 0.2 0.1 0.03\n", | |
"D=4.0 pc 0.0280 0.0108 0.0077 0.0034 0.0006 mJy S/N= 0.06 0.1 0.1 0.08 0.02\n", | |
"D=5.0 pc 0.0179 0.0069 0.0049 0.0022 0.0004 mJy S/N= 0.04 0.07 0.08 0.05 0.01\n" | |
] | |
} | |
], | |
"source": [ | |
"for temperature in (300, 1000, 2000)*u.K:\n", | |
" print(f\"Temperature = {temperature}\")\n", | |
" bb = modeling.blackbody.blackbody_nu(in_x=freqs*u.GHz, temperature=temperature)\n", | |
" for distance in (2, 3, 4, 5)*u.pc:\n", | |
" angscale = np.pi * (constants.R_jup**2 / distance**2).to(u.sr, u.dimensionless_angles())\n", | |
" sn = ((bb*angscale)/([sens_of_freq[freq] for freq in freqs]*u.Jy).to(u.mJy)).decompose()\n", | |
" snstr = \" \".join(\"{0:7.1g}\".format(x) for x in sn)\n", | |
" print(f\"D={distance:6s}\", \" \".join(\"{0:6.4f}\".format(x) for x in (bb * angscale).to(u.mJy).value)+\" mJy\", f\"S/N={snstr}\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 122, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Stars\n", | |
"Temperature = 3000.0 K freqs=(660, 410, 345, 230, 100)\n", | |
"D=1.35 pc 52.4293 20.2733 14.3622 6.3891 1.2090 mJy S/N= 114.2 215.2 235.4 157.4 34.9\n", | |
"D=2.0 pc 23.8881 9.2370 6.5438 2.9110 0.5509 mJy S/N= 52.0 98.1 107.2 71.7 15.9\n", | |
"D=3.0 pc 10.6169 4.1053 2.9083 1.2938 0.2448 mJy S/N= 23.1 43.6 47.7 31.9 7.1\n", | |
"D=4.0 pc 5.9720 2.3093 1.6359 0.7278 0.1377 mJy S/N= 13.0 24.5 26.8 17.9 4.0\n", | |
"Temperature = 4000.0 K freqs=(660, 410, 345, 230, 100)\n", | |
"D=1.35 pc 69.9982 27.0532 19.1628 8.5227 1.6124 mJy S/N= 152.4 287.2 314.1 210.0 46.6\n", | |
"D=2.0 pc 31.8930 12.3261 8.7311 3.8831 0.7346 mJy S/N= 69.4 130.9 143.1 95.7 21.2\n", | |
"D=3.0 pc 14.1746 5.4783 3.8805 1.7258 0.3265 mJy S/N= 30.9 58.2 63.6 42.5 9.4\n", | |
"D=4.0 pc 7.9732 3.0815 2.1828 0.9708 0.1837 mJy S/N= 17.4 32.7 35.8 23.9 5.3\n", | |
"Temperature = 5000.0 K freqs=(660, 410, 345, 230, 100)\n", | |
"D=1.35 pc 87.5672 33.8332 23.9634 10.6563 2.0157 mJy S/N= 190.7 359.2 392.7 262.6 58.2\n", | |
"D=2.0 pc 39.8978 15.4152 10.9183 4.8553 0.9184 mJy S/N= 86.9 163.7 178.9 119.6 26.5\n", | |
"D=3.0 pc 17.7324 6.8512 4.8526 2.1579 0.4082 mJy S/N= 38.6 72.7 79.5 53.2 11.8\n", | |
"D=4.0 pc 9.9745 3.8538 2.7296 1.2138 0.2296 mJy S/N= 21.7 40.9 44.7 29.9 6.6\n", | |
"Temperature = 6000.0 K freqs=(660, 410, 345, 230, 100)\n", | |
"D=1.35 pc 105.1362 40.6131 28.7640 12.7899 2.4190 mJy S/N= 228.9 431.2 471.4 315.2 69.9\n", | |
"D=2.0 pc 47.9027 18.5044 13.1056 5.8274 1.1022 mJy S/N= 104.3 196.5 214.8 143.6 31.9\n", | |
"D=3.0 pc 21.2901 8.2242 5.8247 2.5900 0.4898 mJy S/N= 46.4 87.3 95.5 63.8 14.2\n", | |
"D=4.0 pc 11.9757 4.6261 3.2764 1.4569 0.2755 mJy S/N= 26.1 49.1 53.7 35.9 8.0\n" | |
] | |
} | |
], | |
"source": [ | |
"print(\"Stars\")\n", | |
"for temperature in (3000, 4000, 5000, 6000)*u.K:\n", | |
" print(f\"Temperature = {temperature} freqs={freqs}\")\n", | |
" bb = modeling.blackbody.blackbody_nu(in_x=freqs*u.GHz, temperature=temperature)\n", | |
" for distance in (1.35, 2, 3, 4 )*u.pc:\n", | |
" angscale = np.pi * ((constants.R_sun*1.224)**2 / (distance**2)).to(u.sr, u.dimensionless_angles())\n", | |
" sn = ((bb*angscale)/([sens_of_freq[freq] for freq in freqs]*u.Jy).to(u.mJy)).decompose()\n", | |
" snstr = \" \".join(\"{0:7.1f}\".format(x) for x in sn)\n", | |
" print(f\"D={distance:7s}\", \" \".join(\"{0:8.4f}\".format(x) for x in (bb * angscale).to(u.mJy).value)+\" mJy\", f\"S/N={snstr}\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 123, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"https://gist.github.com/57f83c30d301bf3c3871ce69169c95d0\n" | |
] | |
} | |
], | |
"source": [ | |
"!gist -u 57f83c30d301bf3c3871ce69169c95d0 ALMA_exoplanets.ipynb" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 87, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"{'beamsizeInArcsecs': 0,\n", | |
" 'integrationTimeInSec': 600,\n", | |
" 'observingFrequencyInGHZ': 85,\n", | |
" 'bandwidth': 7.5,\n", | |
" 'dec': 0,\n", | |
" 'numberOfAntennas': '43',\n", | |
" 'antennaArray': 'ARRAY_12M',\n", | |
" 'polarization': 'DUAL',\n", | |
" 'wvIndex': '2'}" | |
] | |
}, | |
"execution_count": 87, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 88, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Frequency:85 Time:600, sensitivity: 2.286018505739588e-05\n", | |
"Frequency:95 Time:600, sensitivity: 2.230161579653174e-05\n", | |
"Frequency:105 Time:600, sensitivity: 2.301726140222891e-05\n", | |
"Frequency:115 Time:600, sensitivity: 4.10242929363441e-05\n", | |
"Frequency:125 Time:600, sensitivity: 2.982375617264369e-05\n", | |
"Frequency:135 Time:600, sensitivity: 2.4226971240212284e-05\n", | |
"Frequency:145 Time:600, sensitivity: 2.4596572716540425e-05\n", | |
"Frequency:155 Time:600, sensitivity: 2.4441512045693435e-05\n", | |
"Frequency:165 Time:600, sensitivity: 2.989707253551504e-05\n", | |
"Frequency:175 Time:600, sensitivity: 3.502665742019074e-05\n", | |
"Frequency:185 Time:600, sensitivity: 0.00020624103899504124\n", | |
"Frequency:195 Time:600, sensitivity: 3.376924988441251e-05\n", | |
"Frequency:205 Time:600, sensitivity: 3.1055128744515516e-05\n", | |
"Frequency:215 Time:600, sensitivity: 3.291462128931775e-05\n", | |
"Frequency:225 Time:600, sensitivity: 3.115880769545341e-05\n", | |
"Frequency:235 Time:600, sensitivity: 3.261382662589016e-05\n", | |
"Frequency:245 Time:600, sensitivity: 3.2413542067361974e-05\n", | |
"Frequency:255 Time:600, sensitivity: 3.3207670155961055e-05\n", | |
"Frequency:265 Time:600, sensitivity: 3.4925736201920825e-05\n", | |
"Frequency:275 Time:600, sensitivity: 3.530261572977587e-05\n", | |
"Frequency:285 Time:600, sensitivity: 4.603001850270528e-05\n", | |
"Frequency:295 Time:600, sensitivity: 4.758958975149275e-05\n", | |
"Frequency:305 Time:600, sensitivity: 5.017346483605373e-05\n", | |
"Frequency:315 Time:600, sensitivity: 5.654944564787147e-05\n", | |
"Frequency:325 Time:600, sensitivity: 0.0015798764798053001\n", | |
"Frequency:335 Time:600, sensitivity: 6.283824255690137e-05\n", | |
"Frequency:345 Time:600, sensitivity: 6.1018028041566896e-05\n", | |
"Frequency:355 Time:600, sensitivity: 7.786755969127102e-05\n", | |
"Frequency:365 Time:600, sensitivity: 9.880408563378989e-05\n", | |
"Frequency:385 Time:600, sensitivity: 0.0010492620514565918\n", | |
"Frequency:395 Time:600, sensitivity: 0.00015644604215451412\n", | |
"Frequency:405 Time:600, sensitivity: 0.00013121247405908467\n", | |
"Frequency:415 Time:600, sensitivity: 0.00014498879496600196\n", | |
"Frequency:425 Time:600, sensitivity: 23188.62973797545\n", | |
"Frequency:435 Time:600, sensitivity: 0.0002482752676326892\n", | |
"Frequency:445 Time:600, sensitivity: 0.010201547636414194\n", | |
"Frequency:455 Time:600, sensitivity: 0.0005017822275201637\n", | |
"Frequency:465 Time:600, sensitivity: 0.00025383468425410806\n", | |
"Frequency:475 Time:600, sensitivity: 0.00569144416308794\n", | |
"Frequency:485 Time:600, sensitivity: 0.0005601872812248147\n", | |
"Frequency:495 Time:600, sensitivity: 0.0004092410890298423\n", | |
"Frequency:605 Time:600, sensitivity: 0.0034174027988636392\n", | |
"Frequency:615 Time:600, sensitivity: 0.003557201685687786\n", | |
"Frequency:625 Time:600, sensitivity: 0.0042597624967414234\n", | |
"Frequency:635 Time:600, sensitivity: 0.001238177567218205\n", | |
"Frequency:645 Time:600, sensitivity: 0.0010781440962922713\n", | |
"Frequency:655 Time:600, sensitivity: 0.0028107345305611953\n", | |
"Frequency:665 Time:600, sensitivity: 0.0009398387582738822\n", | |
"Frequency:675 Time:600, sensitivity: 0.0009541599985893436\n", | |
"Frequency:685 Time:600, sensitivity: 0.0010593817555556227\n", | |
"Frequency:695 Time:600, sensitivity: 0.0013310690185921311\n", | |
"Frequency:705 Time:600, sensitivity: 0.0018265238158653146\n", | |
"Frequency:715 Time:600, sensitivity: 0.08640768307887521\n", | |
"Frequency:795 Time:600, sensitivity: 0.005143586214148306\n", | |
"Frequency:805 Time:600, sensitivity: 0.003367146768426415\n", | |
"Frequency:815 Time:600, sensitivity: 0.002697289222836473\n", | |
"Frequency:825 Time:600, sensitivity: 0.002528071720550005\n", | |
"Frequency:835 Time:600, sensitivity: 0.045171116988885526\n", | |
"Frequency:845 Time:600, sensitivity: 0.002276590756676598\n", | |
"Frequency:855 Time:600, sensitivity: 0.0022451184621373956\n", | |
"Frequency:865 Time:600, sensitivity: 0.0023044150860813154\n", | |
"Frequency:875 Time:600, sensitivity: 0.002402355646605203\n", | |
"Frequency:885 Time:600, sensitivity: 0.0026608888457591824\n", | |
"Frequency:895 Time:600, sensitivity: 0.003822994268668506\n", | |
"Frequency:905 Time:600, sensitivity: 0.006808565501410239\n", | |
"Frequency:915 Time:600, sensitivity: 4447980.573050138\n", | |
"Frequency:925 Time:600, sensitivity: 0.01068635516216156\n", | |
"Frequency:935 Time:600, sensitivity: 0.006605321015769967\n", | |
"Frequency:945 Time:600, sensitivity: 0.009420933085589934\n" | |
] | |
} | |
], | |
"source": [ | |
"url = \"https://asa.alma.cl/SensitivityCalculator/webapi/sensitivity\"\n", | |
"data = {\"beamsizeInArcsecs\":0,\n", | |
" \"integrationTimeInSec\":600,\n", | |
" \"observingFrequencyInGHZ\":345,\n", | |
" \"bandwidth\":7.5,\n", | |
" \"dec\":0,\n", | |
" \"numberOfAntennas\":\"43\",\n", | |
" \"antennaArray\":\"ARRAY_12M\",\n", | |
" \"polarization\":\"DUAL\",\n", | |
" \"wvIndex\":\"2\",\n", | |
" }\n", | |
"sens_of_freq_all = {}\n", | |
"freqs_all = np.arange(85, 950, 10)\n", | |
"for freq in freqs_all:\n", | |
" data['observingFrequencyInGHZ'] = int(freq)\n", | |
" result = requests.post(url, json=data)\n", | |
" try:\n", | |
" sens = result.json()\n", | |
" except Exception as ex:\n", | |
" continue\n", | |
" sens_of_freq_all[freq] = sens['sensitivityInJ']\n", | |
" print(f\"Frequency:{data['observingFrequencyInGHZ']} Time:{data['integrationTimeInSec']}, sensitivity: {sens['sensitivityInJ']}\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 89, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline\n", | |
"import pylab as pl" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 116, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(0, 2e-08)" | |
] | |
}, | |
"execution_count": 116, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 1008x576 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"pl.figure(figsize=(14,8))\n", | |
"sorted_freqs_all = np.sort(np.array(list(sens_of_freq_all.keys())))\n", | |
"pl.plot(sorted_freqs_all, np.array([sens_of_freq_all[freq] for freq in sorted_freqs_all]) / sorted_freqs_all**2)\n", | |
"pl.xlabel(\"Frequency\")\n", | |
"pl.ylabel(\"Relative sensitivity to a blackbody\")\n", | |
"pl.ylim(0, 2e-8)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 118, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(200, 400)" | |
] | |
}, | |
"execution_count": 118, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 1008x576 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"pl.figure(figsize=(14,8))\n", | |
"sorted_freqs_all = np.sort(np.array(list(sens_of_freq_all.keys())))\n", | |
"pl.plot(sorted_freqs_all, np.array([sens_of_freq_all[freq] for freq in sorted_freqs_all]) / sorted_freqs_all**2)\n", | |
"pl.xlabel(\"Frequency\")\n", | |
"pl.ylabel(\"Relative sensitivity to a blackbody\")\n", | |
"pl.ylim(0, 2e-9)\n", | |
"pl.xlim(200, 400)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 119, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(400, 700)" | |
] | |
}, | |
"execution_count": 119, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 1008x576 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"pl.figure(figsize=(14,8))\n", | |
"sorted_freqs_all = np.sort(np.array(list(sens_of_freq_all.keys())))\n", | |
"pl.plot(sorted_freqs_all, np.array([sens_of_freq_all[freq] for freq in sorted_freqs_all]) / sorted_freqs_all**2)\n", | |
"pl.xlabel(\"Frequency\")\n", | |
"pl.ylabel(\"Relative sensitivity to a blackbody\")\n", | |
"pl.ylim(0, 2e-8)\n", | |
"pl.xlim(400, 700)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"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": "3.7.3" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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