From eec1f6c5313dd35eb1da791b6e7f8341df8b81ab Mon Sep 17 00:00:00 2001 From: tcjansen Date: Tue, 4 Jun 2019 16:12:18 -0400 Subject: [PATCH 01/26] Initial commit - I'll give future branches more descriptive names --- CerroParanal-atmosx1.5.txt | 297001 ++++++++++++++++++++++++++++++++++ README.rst | 1 - ccd_QE.csv | 69 + observing_examples.ipynb | 448 + sdss/g.dat | 10001 ++ sdss/i.dat | 10501 ++ sdss/r.dat | 10501 ++ sdss/u.dat | 9051 ++ sdss/z.dat | 10501 ++ 9 files changed, 348073 insertions(+), 1 deletion(-) create mode 100644 CerroParanal-atmosx1.5.txt delete mode 100644 README.rst create mode 100644 ccd_QE.csv create mode 100644 observing_examples.ipynb create mode 100644 sdss/g.dat create mode 100644 sdss/i.dat create mode 100644 sdss/r.dat create mode 100644 sdss/u.dat create mode 100644 sdss/z.dat diff --git a/CerroParanal-atmosx1.5.txt b/CerroParanal-atmosx1.5.txt new file mode 100644 index 0000000..4fd4d6f --- /dev/null +++ b/CerroParanal-atmosx1.5.txt @@ -0,0 +1,297001 @@ 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index a1f1dd2..0000000 --- a/README.rst +++ /dev/null @@ -1 +0,0 @@ -Browse the branches to see the different notebooks I've been working on! \ No newline at end of file diff --git a/ccd_QE.csv b/ccd_QE.csv new file mode 100644 index 0000000..287d209 --- /dev/null +++ b/ccd_QE.csv @@ -0,0 +1,69 @@ +100, 0.0, 0, 0 +309.4560021465238, 70.30747424892068, 16, 40 +319.539015188068, 73.11442326755378, 14, 30.35714285714286 +329.9917387078021, 75.26541144025542, 15, 35.46666666666667 +339.70504127115635, 76.58538173145493, 14, 30.35714285714286 +349.7880543127005, 74.70200948669466, 10, 16.1 +379.53294278525584, 77.10351031374742, 12, 23.66666666666667 +390.00376402070555, 81.79762370567437, 13, 26.153846153846164 +399.57293120532483, 84.52897199856781, 16, 41.1875 +409.3114413012829, 87.18136275806562, 15, 35.46666666666667 +419.59611460365807, 89.04058920481614, 15, 36.26666666666667 +429.14136694965316, 91.16038905508852, 10, 17.700000000000003 +439.5268703824437, 92.49142617037583, 12, 23 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Computing the count rate of observing HAT-P-11 with the APO 3.5m telescope using synphot
" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import astropy.units as u\n", + "from astropy.io import fits\n", + "from astropy.modeling.blackbody import blackbody_lambda\n", + "\n", + "import synphot.units as units\n", + "from synphot.models import BlackBody1D, BlackBodyNorm1D, Empirical1D\n", + "from synphot.spectrum import SourceSpectrum, SpectralElement\n", + "from synphot.observation import Observation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute the photon flux (of HAT-P-11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we are going to download a model spectrum for HAT-P-11 from PHOENIX:" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "T_eff = 4800 # approx temperature of hatp11[K], Bakos et al. 2010\n", + "\n", + "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", + " 'ACES-AGSS-COND-2011-HiRes.fits').format(T_eff=T_eff, log_g=4.5)\n", + "# Unit from PHOENIX model file:\n", + "flux = fits.getdata(url) * (u.erg / u.s / u.cm ** 2 / u.cm)\n", + "\n", + "wavelength_url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')\n", + "wavelengths = fits.getdata(wavelength_url) * u.Angstrom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then construct a `synphot` source spectrum object from the above arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "photlam_hatp11 = SourceSpectrum(Empirical1D,\n", + " points=wavelengths, lookup_table=flux)\n", + "\n", + "# Compute the flux at Earth from the luminosity \n", + "R = 0.683 * u.R_sun # radius in solar units, Deming et al. 2011\n", + "d = 123 * u.lyr # distance to HATP11 in light years from Gaia parallax\n", + "photlam_hatp11 = photlam_hatp11 * float(R / d) ** 2 / np.pi\n", + "\n", + "photlam_hatp11.plot(flux_unit='Jy', left=0, right=60000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(for other methods of constructing source spectra with `synphot`, see the first bulleted list here) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulate the observation with a specific instrument" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Let's observe HAT-P-11 with the ARCTIC instrument on APO's 3.5m telescope." + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "r = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", + "area = np.pi * r ** 2\n", + "gain = 1.9 # the gain of this detector in e-/ADU" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the bandpass, we will use the SDSS filters - obtaining the transmission functions here. \n", + "

To construct a bandpass from a file with synphot, use SpectralElement:

" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sdss = ['u', 'g', 'r', 'i', 'z']\n", + "\n", + "bandpasses = {}\n", + "for band in sdss:\n", + " bp = SpectralElement.from_file('sdss/' + band + '.dat')\n", + " bandpasses[band] = bp\n", + " \n", + " plt.plot(bp.waveset, bp(bp.waveset), label=band)\n", + "plt.legend(loc='lower right')\n", + "plt.xlim(3000, 14000)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we model the attenuation by the atmosphere using the Cerro Paranal model transmittance curves for an airmass of 1.5:" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "atmos = SpectralElement.from_file('CerroParanal-atmosx1.5.txt', wave_unit=\"nm\")\n", + "atmos.plot(left=3000, right=14000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also model the affect of the CCD's quantum efficiency on the spectrum. For this we use the values in the table found in section 3.5 on this page." + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "qe = SpectralElement.from_file('ccd_QE.csv', wave_unit=\"nm\")\n", + "qe._model.lookup_table /= 100 # convert percentages to decimals\n", + "\n", + "qe.plot(left=3000, right=14000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have all of the effects on the source spectrum set, we can combine them together by simply multiplying the SpectralElement objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bp_atmos_qe = {} # setting up a dictionary for the different SDSS bands\n", + "for band in sdss:\n", + " bp_atmos_qe[band] = bandpasses[band] * atmos * qe\n", + " \n", + "bp_atmos_qe['r'].plot(left=3000, right=14000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we convolve the source spectrum with all of these effects using synphot's Observation:" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "obs = {}\n", + "for band in sdss:\n", + " obs[band] = Observation(photlam_hatp11, \n", + " bp_atmos_qe[band],\n", + " binset=bp_atmos_qe[band].waveset,\n", + " force='taper')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the expected count rate for a specific collecting area, use the countrate() method: " + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HAT-P-11 counts in each filter for 10 s exposure:\n", + "u: 542237.6220025131 ct\n", + "g: 16282930.520288832 ct\n", + "r: 30351424.711685903 ct\n", + "i: 33165716.246944867 ct\n", + "z: 17287938.31740302 ct\n" + ] + } + ], + "source": [ + "waverange = (3000, 11000) # according to the ARCTIC webpage, the z band is cut off at 11k AA\n", + "\n", + "print(\"HAT-P-11 counts in each filter for 10 s exposure:\")\n", + "for band in sdss:\n", + " # enforce the wavelength range by setting waverange\n", + " countrate = obs[band].countrate(area=area, waverange=waverange) / gain\n", + " print(band + \":\", countrate * 10 * u.s)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "#
Same instrument and telescope, this time observing TRAPPIST-1
" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "T_eff = 2500 # approx temperature of trappist-1 [K], Delrez et al. 2018\n", + "\n", + "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", + " 'ACES-AGSS-COND-2011-HiRes.fits').format(T_eff=T_eff, log_g=4.5)\n", + "# Unit from PHOENIX model file:\n", + "flux = fits.getdata(url) * (u.erg / u.s / u.cm ** 2 / u.cm)\n", + "\n", + "wavelength_url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", + " 'WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')\n", + "wavelengths = fits.getdata(wavelength_url) * u.Angstrom" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "photlam_trappist1 = SourceSpectrum(Empirical1D,\n", + " points=wavelengths, lookup_table=flux)\n", + "\n", + "# Compute the flux at Earth from the luminosity \n", + "R = 0.121 * u.R_sun # radius of trappist-1 in solar units, Van Grootel et al. 2018\n", + "d = 39.6 * u.lyr # distance to trappist-1 in light years from Gaia parallax\n", + "photlam_trappist1 = photlam_trappist1 * float(R / d) ** 2 / np.pi\n", + "\n", + "photlam_trappist1.plot(flux_unit='Jy', left=0, right=60000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(including the effects of the QE and atmosphere)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAPPIST-1 counts in each filter for 10s exposure:\n", + "u: 95.53882045856909 ct\n", + "g: 4496.7625906313915 ct\n", + "r: 16067.225032615606 ct\n", + "i: 182196.3013159787 ct\n", + "z: 256581.51481196267 ct\n" + ] + } + ], + "source": [ + "print(\"TRAPPIST-1 counts in each filter for 10s exposure:\")\n", + "for band in sdss:\n", + " bp = SpectralElement.from_file('sdss/' + band + '.dat')\n", + " obs = Observation(photlam_trappist1, bp * qe * atmos, binset=bp.waveset)\n", + " \n", + " countrate = obs.countrate(area=area, waverange=waverange) / gain\n", + " print(band + \":\", countrate * 10 * u.s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "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": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/sdss/g.dat b/sdss/g.dat new file mode 100644 index 0000000..ab4ec46 --- /dev/null +++ b/sdss/g.dat @@ -0,0 +1,10001 @@ + 3000 0.000000 + 3001 0.000000 + 3002 0.000000 + 3003 0.000000 + 3004 0.000000 + 3005 0.000000 + 3006 0.000000 + 3007 0.000000 + 3008 0.000000 + 3009 0.000000 + 3010 0.000000 + 3011 0.000000 + 3012 0.000000 + 3013 0.000000 + 3014 0.000000 + 3015 0.000000 + 3016 0.000000 + 3017 0.000000 + 3018 0.000000 + 3019 0.000000 + 3020 0.000000 + 3021 0.000000 + 3022 0.000000 + 3023 0.000000 + 3024 0.000000 + 3025 0.000000 + 3026 0.000000 + 3027 0.000000 + 3028 0.000000 + 3029 0.000000 + 3030 0.000000 + 3031 0.000000 + 3032 0.000000 + 3033 0.000000 + 3034 0.000000 + 3035 0.000000 + 3036 0.000000 + 3037 0.000000 + 3038 0.000000 + 3039 0.000000 + 3040 0.000000 + 3041 0.000000 + 3042 0.000000 + 3043 0.000000 + 3044 0.000000 + 3045 0.000000 + 3046 0.000000 + 3047 0.000000 + 3048 0.000000 + 3049 0.000000 + 3050 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2019 17:34:05 -0400 Subject: [PATCH 02/26] Added comparison plot, more explicit variable names --- observing_examples.ipynb | 194 +++++++++++++++++++++++++++++---------- 1 file changed, 144 insertions(+), 50 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index a5e4591..7ee09cf 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -19,10 +19,9 @@ "\n", "import astropy.units as u\n", "from astropy.io import fits\n", - "from astropy.modeling.blackbody import blackbody_lambda\n", "\n", "import synphot.units as units\n", - "from synphot.models import BlackBody1D, BlackBodyNorm1D, Empirical1D\n", + "from synphot.models import Empirical1D\n", "from synphot.spectrum import SourceSpectrum, SpectralElement\n", "from synphot.observation import Observation" ] @@ -43,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -90,9 +89,9 @@ " points=wavelengths, lookup_table=flux)\n", "\n", "# Compute the flux at Earth from the luminosity \n", - "R = 0.683 * u.R_sun # radius in solar units, Deming et al. 2011\n", - "d = 123 * u.lyr # distance to HATP11 in light years from Gaia parallax\n", - "photlam_hatp11 = photlam_hatp11 * float(R / d) ** 2 / np.pi\n", + "stellar_radius = 0.683 * u.R_sun # radius in solar units, Deming et al. 2011\n", + "distance = 123 * u.lyr # distance to HATP11 in light years from Gaia parallax\n", + "photlam_hatp11 = photlam_hatp11 * float(stellar_radius / distance) ** 2 / np.pi\n", "\n", "photlam_hatp11.plot(flux_unit='Jy', left=0, right=60000)" ] @@ -120,12 +119,12 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ - "r = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", - "area = np.pi * r ** 2\n", + "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", + "aperture_area = np.pi * aperture_radius ** 2\n", "gain = 1.9 # the gain of this detector in e-/ADU" ] }, @@ -139,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -178,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -195,8 +194,8 @@ } ], "source": [ - "atmos = SpectralElement.from_file('CerroParanal-atmosx1.5.txt', wave_unit=\"nm\")\n", - "atmos.plot(left=3000, right=14000)" + "atmosphere = SpectralElement.from_file('CerroParanal-atmosx1.5.txt', wave_unit=\"nm\")\n", + "atmosphere.plot(left=3000, right=14000)" ] }, { @@ -208,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -225,10 +224,10 @@ } ], "source": [ - "qe = SpectralElement.from_file('ccd_QE.csv', wave_unit=\"nm\")\n", - "qe._model.lookup_table /= 100 # convert percentages to decimals\n", + "quantum_efficiency = SpectralElement.from_file('ccd_QE.csv', wave_unit=\"nm\")\n", + "quantum_efficiency._model.lookup_table /= 100 # convert percentages to decimals\n", "\n", - "qe.plot(left=3000, right=14000)" + "quantum_efficiency.plot(left=3000, right=14000)" ] }, { @@ -240,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -259,7 +258,7 @@ "source": [ "bp_atmos_qe = {} # setting up a dictionary for the different SDSS bands\n", "for band in sdss:\n", - " bp_atmos_qe[band] = bandpasses[band] * atmos * qe\n", + " bp_atmos_qe[band] = bandpasses[band] * atmosphere * quantum_efficiency\n", " \n", "bp_atmos_qe['r'].plot(left=3000, right=14000)" ] @@ -273,15 +272,15 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 82, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "obs = {}\n", + "observation = {}\n", "for band in sdss:\n", - " obs[band] = Observation(photlam_hatp11, \n", + " observation[band] = Observation(photlam_hatp11, \n", " bp_atmos_qe[band],\n", " binset=bp_atmos_qe[band].waveset,\n", " force='taper')" @@ -296,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -304,22 +303,25 @@ "output_type": "stream", "text": [ "HAT-P-11 counts in each filter for 10 s exposure:\n", - "u: 542237.6220025131 ct\n", - "g: 16282930.520288832 ct\n", - "r: 30351424.711685903 ct\n", - "i: 33165716.246944867 ct\n", - "z: 17287938.31740302 ct\n" + "u: 542237 ct\n", + "g: 16282930 ct\n", + "r: 30351424 ct\n", + "i: 33165716 ct\n", + "z: 17287938 ct\n" ] } ], "source": [ "waverange = (3000, 11000) # according to the ARCTIC webpage, the z band is cut off at 11k AA\n", "\n", + "counts_hatp11 = {}\n", "print(\"HAT-P-11 counts in each filter for 10 s exposure:\")\n", "for band in sdss:\n", " # enforce the wavelength range by setting waverange\n", - " countrate = obs[band].countrate(area=area, waverange=waverange) / gain\n", - " print(band + \":\", countrate * 10 * u.s)" + " countrate = observation[band].countrate(area=aperture_area, waverange=waverange) / gain\n", + " counts_hatp11[band] = countrate * 10 * u.s\n", + " \n", + " print(band + \":\", int(counts_hatp11[band].value), \"ct\")" ] }, { @@ -333,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -352,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -373,9 +375,9 @@ " points=wavelengths, lookup_table=flux)\n", "\n", "# Compute the flux at Earth from the luminosity \n", - "R = 0.121 * u.R_sun # radius of trappist-1 in solar units, Van Grootel et al. 2018\n", - "d = 39.6 * u.lyr # distance to trappist-1 in light years from Gaia parallax\n", - "photlam_trappist1 = photlam_trappist1 * float(R / d) ** 2 / np.pi\n", + "stellar_radius = 0.121 * u.R_sun # radius of trappist-1 in solar units, Van Grootel et al. 2018\n", + "distance = 39.6 * u.lyr # distance to trappist-1 in light years from Gaia parallax\n", + "photlam_trappist1 = photlam_trappist1 * float(stellar_radius / distance) ** 2 / np.pi\n", "\n", "photlam_trappist1.plot(flux_unit='Jy', left=0, right=60000)" ] @@ -389,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -397,30 +399,122 @@ "output_type": "stream", "text": [ "TRAPPIST-1 counts in each filter for 10s exposure:\n", - "u: 95.53882045856909 ct\n", - "g: 4496.7625906313915 ct\n", - "r: 16067.225032615606 ct\n", - "i: 182196.3013159787 ct\n", - "z: 256581.51481196267 ct\n" + "u: 95 ct\n", + "g: 4496 ct\n", + "r: 16067 ct\n", + "i: 182196 ct\n", + "z: 256581 ct\n" ] } ], "source": [ + "counts_trappist1 = {}\n", "print(\"TRAPPIST-1 counts in each filter for 10s exposure:\")\n", "for band in sdss:\n", - " bp = SpectralElement.from_file('sdss/' + band + '.dat')\n", - " obs = Observation(photlam_trappist1, bp * qe * atmos, binset=bp.waveset)\n", + " bandpass = SpectralElement.from_file('sdss/' + band + '.dat')\n", + " observation = Observation(photlam_trappist1,\n", + " bandpass * quantum_efficiency * atmosphere,\n", + " binset=bp.waveset)\n", + " \n", + " countrate = observation.countrate(area=aperture_area, waverange=waverange) / gain\n", + " counts_trappist1[band] = countrate * 10 * u.s\n", " \n", - " countrate = obs.countrate(area=area, waverange=waverange) / gain\n", - " print(band + \":\", countrate * 10 * u.s)" + " print(band + \":\", int(counts_trappist1[band].value), \"ct\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### We know from observations using AGILE on APO's 3.5m telescope that the actual count values for these stars taken on {date} are:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HAT-P-11 in r-band: 34.0 million counts\n" + ] + } + ], + "source": [ + "hatp11_r_observed = 34000000\n", + "print('HAT-P-11 in r-band:', hatp11_r_observed / 1e6, 'million counts')" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAPPIST-1 in z-band: 203.0 thousand counts\n" + ] + } + ], + "source": [ + "trappist1_z_observed = 203000\n", + "print('TRAPPIST-1 in z-band:', trappist1_z_observed / 1e3, 'thousand counts')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparing our expected values to the observed values on a 1-1 line:" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(trappist1_z_observed, counts_trappist1['z'], color='r', s=100, label='TRAPPIST1')\n", + "plt.scatter(hatp11_r_observed, counts_hatp11['r'], color='b', s=100, label=\"HATP11\")\n", + "\n", + "# one-to-one line\n", + "plt.plot(np.linspace(1e5, 1e8, 20), np.linspace(1e5, 1e8, 20), color='black')\n", + "\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "\n", + "plt.ylabel('synphot counts', size='14')\n", + "plt.xlabel('observed counts', size='14')\n", + "\n", + "plt.legend()" + ] } ], "metadata": { From fad52b0f2e888295986a3e054fa59a7e70989b85 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Wed, 5 Jun 2019 20:07:02 -0400 Subject: [PATCH 03/26] Query sdss filters from SVO, get rid of files --- observing_examples.ipynb | 141 ++++++++++++++++++++------------------- 1 file changed, 73 insertions(+), 68 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 7ee09cf..416a21b 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -19,6 +19,7 @@ "\n", "import astropy.units as u\n", "from astropy.io import fits\n", + "from astropy.utils.data import download_file\n", "\n", "import synphot.units as units\n", "from synphot.models import Empirical1D\n", @@ -42,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -68,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -119,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -132,18 +133,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For the bandpass, we will use the SDSS filters - obtaining the transmission functions here. \n", + "For the bandpass we will use the SDSS r- and z- filters whose transmission functions we query from SVO. \n", "

To construct a bandpass from a file with synphot, use SpectralElement:

" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -155,17 +156,20 @@ } ], "source": [ - "sdss = ['u', 'g', 'r', 'i', 'z']\n", + "svo_link = 'http://svo2.cab.inta-csic.es/theory/fps/fps.php?ID=SLOAN/SDSS.'\n", + "sdss = ['r', 'z'] # only want r- and z-bands for now\n", "\n", "bandpasses = {}\n", "for band in sdss:\n", - " bp = SpectralElement.from_file('sdss/' + band + '.dat')\n", + " local_path = download_file(svo_link + band)\n", + " bp = SpectralElement.from_file(local_path)\n", " bandpasses[band] = bp\n", " \n", " plt.plot(bp.waveset, bp(bp.waveset), label=band)\n", "plt.legend(loc='lower right')\n", "plt.xlim(3000, 14000)\n", - "plt.show()" + "plt.show()\n", + " " ] }, { @@ -177,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -207,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -239,12 +243,12 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -272,15 +276,13 @@ }, { "cell_type": "code", - "execution_count": 82, - "metadata": { - "scrolled": true - }, + "execution_count": 69, + "metadata": {}, "outputs": [], "source": [ - "observation = {}\n", - "for band in sdss:\n", - " observation[band] = Observation(photlam_hatp11, \n", + "band = 'r' # only want to know about the r-band\n", + "\n", + "observation = Observation(photlam_hatp11, \n", " bp_atmos_qe[band],\n", " binset=bp_atmos_qe[band].waveset,\n", " force='taper')" @@ -290,38 +292,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To get the expected count rate for a specific collecting area, use the countrate() method: " + "To get the expected count rate for a specific collecting area, use the countrate() method on the observation object: " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "waverange = (3000, 11300) # according to the ARCTIC webpage, the z band is cut off around 11k AA\n", + "\n", + "countrate = observation.countrate(area=aperture_area, waverange=waverange) / gain" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many counts can we expect in a 10s exposure?" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "HAT-P-11 counts in each filter for 10 s exposure:\n", - "u: 542237 ct\n", - "g: 16282930 ct\n", - "r: 30351424 ct\n", - "i: 33165716 ct\n", - "z: 17287938 ct\n" + "HAT-P-11 counts in the r-band, 10 s exposure:\n", + "12480841 cts\n" ] } ], "source": [ - "waverange = (3000, 11000) # according to the ARCTIC webpage, the z band is cut off at 11k AA\n", + "counts_hatp11 = countrate * 10 * u.s\n", "\n", - "counts_hatp11 = {}\n", - "print(\"HAT-P-11 counts in each filter for 10 s exposure:\")\n", - "for band in sdss:\n", - " # enforce the wavelength range by setting waverange\n", - " countrate = observation[band].countrate(area=aperture_area, waverange=waverange) / gain\n", - " counts_hatp11[band] = countrate * 10 * u.s\n", - " \n", - " print(band + \":\", int(counts_hatp11[band].value), \"ct\")" + "print(\"HAT-P-11 counts in the r-band, 10 s exposure:\")\n", + "print(int(counts_hatp11.value), \"cts\")" ] }, { @@ -335,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -354,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -386,40 +396,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "(including the effects of the QE and atmosphere)" + "This time I just want to know about observations in the z-band:" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TRAPPIST-1 counts in each filter for 10s exposure:\n", - "u: 95 ct\n", - "g: 4496 ct\n", - "r: 16067 ct\n", - "i: 182196 ct\n", - "z: 256581 ct\n" + "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", + "13401 cts\n" ] } ], "source": [ - "counts_trappist1 = {}\n", - "print(\"TRAPPIST-1 counts in each filter for 10s exposure:\")\n", - "for band in sdss:\n", - " bandpass = SpectralElement.from_file('sdss/' + band + '.dat')\n", - " observation = Observation(photlam_trappist1,\n", - " bandpass * quantum_efficiency * atmosphere,\n", - " binset=bp.waveset)\n", - " \n", - " countrate = observation.countrate(area=aperture_area, waverange=waverange) / gain\n", - " counts_trappist1[band] = countrate * 10 * u.s\n", - " \n", - " print(band + \":\", int(counts_trappist1[band].value), \"ct\")" + "band = 'z'\n", + "observation = Observation(photlam_trappist1, \n", + " bp_atmos_qe[band],\n", + " binset=bp_atmos_qe[band].waveset,\n", + " force='taper')\n", + "\n", + "countrate = observation.countrate(area=aperture_area, waverange=waverange) / gain\n", + "counts_trappist1 = countrate * 10 * u.s\n", + "\n", + "print(\"TRAPPIST-1 counts in the z-band, 10 s exposure:\")\n", + "print(int(counts_trappist1.value), \"cts\")" ] }, { @@ -431,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -449,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -474,22 +479,22 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 89, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -501,8 +506,8 @@ } ], "source": [ - "plt.scatter(trappist1_z_observed, counts_trappist1['z'], color='r', s=100, label='TRAPPIST1')\n", - "plt.scatter(hatp11_r_observed, counts_hatp11['r'], color='b', s=100, label=\"HATP11\")\n", + "plt.scatter(trappist1_z_observed, counts_trappist1, color='r', s=100, label='TRAPPIST1')\n", + "plt.scatter(hatp11_r_observed, counts_hatp11, color='b', s=100, label=\"HATP11\")\n", "\n", "# one-to-one line\n", "plt.plot(np.linspace(1e5, 1e8, 20), np.linspace(1e5, 1e8, 20), color='black')\n", From 01619ef18ace78fbd5025ba3933a1d38ade0b28c Mon Sep 17 00:00:00 2001 From: tcjansen Date: Thu, 6 Jun 2019 18:53:45 -0400 Subject: [PATCH 04/26] Added Gaia query for hat-p-11 parameters --- observing_examples.ipynb | 102 +- sdss/g.dat | 10001 ----------------------------------- sdss/i.dat | 10501 ------------------------------------- sdss/r.dat | 10501 ------------------------------------- sdss/u.dat | 9051 -------------------------------- sdss/z.dat | 10501 ------------------------------------- 6 files changed, 70 insertions(+), 50587 deletions(-) delete mode 100644 sdss/g.dat delete mode 100644 sdss/i.dat delete mode 100644 sdss/r.dat delete mode 100644 sdss/u.dat delete mode 100644 sdss/z.dat diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 416a21b..a32540a 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -20,6 +20,9 @@ "import astropy.units as u\n", "from astropy.io import fits\n", "from astropy.utils.data import download_file\n", + "from astropy.coordinates import SkyCoord\n", + "\n", + "from astroquery.gaia import Gaia\n", "\n", "import synphot.units as units\n", "from synphot.models import Empirical1D\n", @@ -38,16 +41,54 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we are going to download a model spectrum for HAT-P-11 from PHOENIX:" + "First we get the star's parameters using astroquery's Gaia query: " ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query finished.\n" + ] + } + ], + "source": [ + "ID = 'HAT-P-11'\n", + "\n", + "coord = SkyCoord.from_name(ID)\n", + "width = u.Quantity(1, u.arcmin) # width / height of search\n", + "height = u.Quantity(1, u.arcmin)\n", + "search_results = Gaia.query_object_async(coordinate=coord, width=width, height=height)\n", + "\n", + "# the queried star will be the one nearest to the given coordinates\n", + "search_results.add_index('dist', unique=True)\n", + "hatp11_info = search_results.loc['dist', min(search_results['dist'])]\n", + "\n", + "# now get the parameters\n", + "stellar_radius = hatp11_info['radius_val'] * u.R_sun\n", + "distance = (1 / (hatp11_info['parallax']) * 1000) * u.pc\n", + "T_eff = hatp11_info['teff_val'] * u.K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we are going to download a model spectrum for HAT-P-11 from PHOENIX:" + ] + }, + { + "cell_type": "code", + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ - "T_eff = 4800 # approx temperature of hatp11[K], Bakos et al. 2010\n", + "T_eff = round(T_eff.value/100) * 100 # round to nearest 100 K\n", "\n", "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", @@ -69,12 +110,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 116, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -89,9 +130,6 @@ "photlam_hatp11 = SourceSpectrum(Empirical1D,\n", " points=wavelengths, lookup_table=flux)\n", "\n", - "# Compute the flux at Earth from the luminosity \n", - "stellar_radius = 0.683 * u.R_sun # radius in solar units, Deming et al. 2011\n", - "distance = 123 * u.lyr # distance to HATP11 in light years from Gaia parallax\n", "photlam_hatp11 = photlam_hatp11 * float(stellar_radius / distance) ** 2 / np.pi\n", "\n", "photlam_hatp11.plot(flux_unit='Jy', left=0, right=60000)" @@ -120,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -139,12 +177,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -156,12 +194,12 @@ } ], "source": [ - "svo_link = 'http://svo2.cab.inta-csic.es/theory/fps/fps.php?ID=SLOAN/SDSS.'\n", "sdss = ['r', 'z'] # only want r- and z-bands for now\n", + "apo_sdss_link = 'http://filters.apo.nmsu.edu/data/SDSS/'\n", "\n", "bandpasses = {}\n", "for band in sdss:\n", - " local_path = download_file(svo_link + band)\n", + " local_path = download_file(apo_sdss_link + band + '.dat')\n", " bp = SpectralElement.from_file(local_path)\n", " bandpasses[band] = bp\n", " \n", @@ -181,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -211,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -243,12 +281,12 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 121, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -276,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ @@ -315,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -323,7 +361,7 @@ "output_type": "stream", "text": [ "HAT-P-11 counts in the r-band, 10 s exposure:\n", - "12480841 cts\n" + "38324129 cts\n" ] } ], @@ -345,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -401,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -409,7 +447,7 @@ "output_type": "stream", "text": [ "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "13401 cts\n" + "256581 cts\n" ] } ], @@ -436,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -454,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -479,22 +517,22 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 77, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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0.929640 -13448 0.929631 -13449 0.929618 -13450 0.929600 -13451 0.929577 -13452 0.929548 -13453 0.929515 -13454 0.929477 -13455 0.929437 -13456 0.929393 -13457 0.929347 -13458 0.929299 -13459 0.929250 -13460 0.929200 -13461 0.929150 -13462 0.929101 -13463 0.929053 -13464 0.929007 -13465 0.928963 -13466 0.928922 -13467 0.928885 -13468 0.928852 -13469 0.928823 -13470 0.928800 -13471 0.928782 -13472 0.928769 -13473 0.928757 -13474 0.928747 -13475 0.928735 -13476 0.928721 -13477 0.928702 -13478 0.928676 -13479 0.928643 -13480 0.928600 -13481 0.928546 -13482 0.928482 -13483 0.928410 -13484 0.928330 -13485 0.928246 -13486 0.928158 -13487 0.928068 -13488 0.927977 -13489 0.927887 -13490 0.927800 -13491 0.927717 -13492 0.927639 -13493 0.927567 -13494 0.927501 -13495 0.927443 -13496 0.927394 -13497 0.927354 -13498 0.927325 -13499 0.927306 -13500 0.927300 From b6a355ea910bda37a526c438cbca0e80a7d257f3 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Thu, 6 Jun 2019 19:39:56 -0400 Subject: [PATCH 05/26] Cite Gillon 2017 for trappist-1 parameters --- observing_examples.ipynb | 74 +++++++++++++++++++++++----------------- 1 file changed, 43 insertions(+), 31 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index a32540a..466cdaa 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -71,8 +71,10 @@ "\n", "# now get the parameters\n", "stellar_radius = hatp11_info['radius_val'] * u.R_sun\n", + "# divide 1 AU by parallax (arcseconds) to get distance in parsecs.\n", + "# parallax is given in milliarcseconds, so multiply by 1000: \n", "distance = (1 / (hatp11_info['parallax']) * 1000) * u.pc\n", - "T_eff = hatp11_info['teff_val'] * u.K" + "T_eff = hatp11_info['teff_val']" ] }, { @@ -84,15 +86,15 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "T_eff = round(T_eff.value/100) * 100 # round to nearest 100 K\n", + "T_eff = round(T_eff, -2) # round to nearest 100 K\n", "\n", "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", - " 'ACES-AGSS-COND-2011-HiRes.fits').format(T_eff=T_eff, log_g=4.5)\n", + " 'ACES-AGSS-COND-2011-HiRes.fits').format(T_eff=int(T_eff), log_g=4.5)\n", "# Unit from PHOENIX model file:\n", "flux = fits.getdata(url) * (u.erg / u.s / u.cm ** 2 / u.cm)\n", "\n", @@ -110,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -130,6 +132,7 @@ "photlam_hatp11 = SourceSpectrum(Empirical1D,\n", " points=wavelengths, lookup_table=flux)\n", "\n", + "# Scale the flux to get the value at Earth\n", "photlam_hatp11 = photlam_hatp11 * float(stellar_radius / distance) ** 2 / np.pi\n", "\n", "photlam_hatp11.plot(flux_unit='Jy', left=0, right=60000)" @@ -158,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -219,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -249,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -281,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -314,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -383,11 +386,11 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "T_eff = 2500 # approx temperature of trappist-1 [K], Delrez et al. 2018\n", + "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", "\n", "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", @@ -401,13 +404,22 @@ ] }, { - "cell_type": "code", - "execution_count": 126, + "cell_type": "markdown", "metadata": {}, + "source": [ + "Unfortunately Gaia does not have all the parameters we need for TRAPPIST-1, so we use the values cited in Gillon 2017." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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GY0ySs2eZjYkoZOIRkQWEfpizGFgJ/E1Vbax4YwKs3LqHVo2yaVq/jt+hGJOQwl3xXBJhvxNxulifGsuAjEl25z/+JQALhl1E43rhk8+d787lmJYNud16xZk0Ei7xrIswBtpKEbGuzcYEKNhRcRuU616dyfs3nRW2/AdzNgBwVe8OtGmSfvfGTHoK16ttsoj8VkQqDYsjInVFpJ+IvAas8jY8Y5JLeSIBmLV2x2HbZ67ZzknDxh02wsHvRs31PDZjEkW4K54BOA+NviMiHYGdQD2cEQPGA0+oqv21GBPGvPWHBvq49e3Z7Nh3gMKiEuas38F5XQ/NYVh0sMyzGBZu2MUTE5bTtEEdzu7Uist7tQ9Z9mBpGdv2FHNk0/qexWNMyMTjjirwLPCsO0RNK2B/tEPmmPT0/DXW+hpo8IivK5Y/mb+JI33oan7XqHks21wIwAezN3BOl1Y0zM6i+GApLRtlVyr7wMeLeGfGOuYPu4gmEe5PGVNT0Y5ccFBVN1nSMeGc17U1/U84AoBTj27mczTJZfa6HXwU0ExXE9NXfU/O0DHMda+yysqU9dv3cbCs8tXU6Y9M4sQ/j6PXQxMBWL65kK/znYFQP1/qjLCwr7i00j7rt+/j7+OWYlNfmVjwcgZSk2b+c33viuXf9uvsYySJT6HSh7gq3PFu5Jbr7XsPsKe4hJyhY3h07FJ3X2XZd4V8vsyZBX7aSieJPDVpBX0em8yqrXtDHq+0TLnoiSlc/dL0oNvLypTbR86hz2OTGTF5JU9OXBFtFY0JKZqRC4ypNrGZaoLaU1QCwPWvzqT/CW3Dly0u4a5Rc3nohydRNyuDHn8ZD0BOywYA/PuLlfxxQDceHrOEl6au5vSOLQB4bOwyHhu7LKp4Ov3p0Bi+G3fuZ/NuZ5SFK5+fxv/16kBGhvDx3EPTaD01aQVtmmQz5LSj2banmK2FxZzYrmmUtTfGETHxiEh3VV1cZd15qvqFZ1GZqOQMHcMf+nfllr52dZEsCotLKpbHLQo+cOgLU1bSr1tbpq7YyrhFmynYsZ9FG3dXbF/z/b5K5V+a6ozTF9iVuybOGv55xfL67ft5fMLyoOXu/XAh9354aBqsaUP7cVQz64xgohdNU9soEfmjOOqLyL+Av3kdmAmv6KDTBv/3cdF9szXJ4dMFm3jk06X8+Nmv2XvA+T8OTDrhbNhZu8RTU2cN/5yZa7azcMMuCotsIjwTWTRNbacDjwLTgMbAW8DZXgZlInt4zBK/QzAeuPmt2QDsLiqJ6kvFgCeneB1SVK587puK5ZE3nsEZx7ak6GAp2VkZiFizq6ksmiueg8B+oD7OczyrVdW7hw5MVN4IMwx/QrDPmrhY+l2h3yEcZsgL31KwYx/d7h/LW9PX+R2OSUDRJJ6ZOInnNOAc4CoRec/TqIwxSe2HI6YBMHbhdz5HYhJRNE1tN6hq+SxW3wGDReQaD2My1fS/eRsjFzImjrbtcXrHTc3fxupte8nOyrAOCKZCNIlnS9Xx2oAvvQjGRKe4pPLDfb99Z45PkRzSt2trv0MwCarvP74AYM3wQf4GYhJGNE1tY4BP3N+TcAYG/czLoEx4f4/yGY14yqhyA7lLm0Y+RWIS1YX/tO+rxhEx8ajqSap6svu7C9AbmOp9aCaU8uc2Eln75g38DsEkmBVb9rClsIjf/3ceYxdu8jsc46NqD5mjqrNxOhoYY0y19H54Eu/NKuA3b85m/4FSnvl8BSWl1kk23UQzcsGdAS8zgJ7AVs8iMsakheMfGAtAy0bZXNW76m1kk8qi6VzQOGC5BOdez/vehGMi+d7tLWRMqig+WBq5kEkpEROPqv4lHoGY6KzeFnqkYT91ss4EpoaG/W8xTRvUYdbaHdwz8HgaZtvYxaku5P+wiPwPZ/T2oFT1Mk8iMiHNWruD/C17/A4jqD/07+p3CCaJ/e7deQC0aVyP287v4nM0xmvhvlr8I25RmKhc/u9pfocQUp1Mm9rJ1N4/Jyxn7MLv+PT2Pn6HYjwULvGsVlUbaMkYE1eLN0U3GrdJXuG+pn5UviAi1pnAGBM3H83ZwNrvE/N+pqm9cIkn8FH0Y2tycBEZICLLRCRfRIYG2Z4tIu+626eLSI67vqWITBaRPSLyTJV9eonIAnefp8XGXDcm5dzx7lx+8PcvOO7ez/hswSbytxwahXvysi3sP2A94ZJZuMSjIZajIiKZwAhgINAdZ1Tr7lWK3QDsUNXOwBM48/4AFAH3A78Pcuh/AzcCXdyfAdWNzRiTHA6UlnHTW7O54J9TUFXytxRy/aszuffDBX6HZmohXOLpISK7RaQQONld3i0ihSISTSNsbyBfVVep6gFgJDC4SpnBwGvu8nvA+SIiqrpXVafiJKAKInIk0ERVv1FVBV4HfhhFLMZDl/U4yu8QTBp45vN8CoucqcNXbnV6d94xcg7PfbnSz7BMDYTsXKCqmbU8djtgfcDrApzZTIOWUdUSEdkFtAS2hTlmQZVjtqtlnKaWhl12gt8hmDTw+ITlPD5hOQAHS51GmI/mboS5G/nNDzqx/0ApGRmQnVXbjy7jNS/7wAa791K1yS6aMjUqLyI3ikieiORt3Woj/HipRcO6fodg0kzVnm+rt+3l+AfG0vW+sTb5XBLwMvEUAB0CXrcHqs5YVlFGRLKApsD2CMdsH+GYAKjqC6qaq6q5rVsn/1wxu4sO+h2CMQll+qrvK5bL5/wB+G/e+iClTSLxMvHMBLqISEcRqQsMAUZXKTMauNZdvgL43L13E5SqbgIKReQMtzfbz4GPYx964pm/fpffISS0p4ac4ncIJs5+8sK3QdeXqTJ3/U7emr42zhGZaHk2KJJ7z+ZWYByQCbyiqotE5EEgT1VHAy8Db4hIPs6VzpDy/UVkDdAEqCsiPwQuUtXFwE3Af4D6OBPSpcWkdD97ebrfISS0Mzu19DsEkyAmL9vK5GVO83rn1o04sV1T6mZl2OgaCcTT0fhU9VPg0yrrHghYLgKuDLFvToj1ecCJsYvSJLv2zevTpnE9v8MwCaj8qqhVo7rk3Xch2/YU06pRNnuLSyhVpUm9Oj5HmJ7sK4BJevXrWC8mE962PQf4YtkWch+ayORlW+jxl/GcPGy832GlLUs8xpi08OjYZQDMXbeTkrJqPxNvYsgSj0kZ7990lt8hmAS2xO2CHTi1yJZC5xn19dv3kTN0DF+tsEcv4sESj6mVIad1iFzImAQyZsGmiuXeD08CYOYa5ymO575cyQMfL2R+wU5em7aGPo99zqQlm32JM5XZVH8m6XVpa7Ofmpr75/hltGqcDcDX+d/zdf73vP7Noa7Yd/13HnMfuMiv8FKSJR6T9P5+RQ8ATm7f1OdITDJ6+vP8sNt37jtI0cFSylRpUNc+MmPBmtpM0muY7XwY1MnMIP/hgT5HY1JRt/vH0v2BcazauoeJiys3ve0uOsgF//ySRRvtIe9oWeIxKSXLHhI0Hur3+Jf88vU8PpxTUJGAvl35Pflb9vDEhBU+R5c87LrRGGOq6XfvzgNgzfBBPkeSnOzrYRJYuCE5L+HrZNrksCa13fPB/Eqv9x8opWDHPp+iSR6WeJLAJf+a6ncIIdkYaSadvTNjfcXcQBOXbOb4B8ZyzqOTOVBS5nNkic0Sj6mVszq18vX8V/W254iMv/71+eH3dgaP+JrR84LO2EJpmbJ8c6HXYSU0SzzGMxnifVPbcW0be34OY8JZ+t3hSWTJpt3c9s6citEQPpnvJKFFG3fR6U+fctETU1j63e7D9ksX1rnA1EqzBqFH983OyqDYmhxMmrvm5RkA9OvWhpEzDk1St2lXEd2OaOJXWL6yKx5TK+HmOPlln2PjGIkxie2wcUkVxi7cxP4Dpb7E4ydLPMYz5Q92eqlekCkRzuua/FOdm9SjqkxffWi67vkFu/jNm7N54OOFlJSWoarc+e5cLk3gzkSxYoknAakqr01bw+6ig36HkvCu7NX+sHWvXHuaD5EYE95Jw8azfPOhkbFXbXOW/zurgM73fsazX6zkgzkbWBDw+MSkJZs5WJp6zdWWeBLQmAWb+PPoRZw8bHzFqLkmuGAjFWRk2PNDJvF9PLdyr7eXvlpVsayqTF2xjRtey+PJictRVb5asZWyFJlHyBJPAloe0Evm3g8XeHaebkdYjzBjEsWOfYdaOAY8+RVrvt8LQMGO/fzo2Wlc8/IM3pq+NtTuScUSTwJatW1vxfLGnUWenae1OxR8TdWrk7hvn94dW/gdgjE1tmxzIfd9tBBwrozmrt8JwLrtqTEqQuJ+cqSxT+YfmqhqT3GJj5GE9+ltfcJu7+vxTf4m9UJ3Xhj16zM9PbcxpuYs8ZgaO7Z1+AnYIm2vrSt62agFJr0s3JAaD51a4jHGmCSRv3VP5EJJwBKPMcYkCVXr1WaS0Gk5zSuWk/093KeLvwOUGhNvKdKb2hJPOlOS+13ct1sbv0MwJq7sisckpab16/odQtxYzzaTauyKxySl1o0PJZ7afHnq0b5pDKLxVouG6ZNkTXqwKx5jjDFxlRppxxJPWqvNl6ecVg1jF4gxJiopcsHjbeIRkQEiskxE8kVkaJDt2SLyrrt9uojkBGy7x12/TET6B6xfIyILRGSuiOR5GX9qis0Amke3aBCT49RUHCY3NSbhpEpTm2cTpohIJjACuBAoAGaKyGhVXRxQ7AZgh6p2FpEhwKPAT0SkOzAEOAE4CpgoIsepavmMSX1VdZtXsaeLZO7VtmBY/4hlLDmZVLM3RSaN8/KKpzeQr6qrVPUAMBIYXKXMYOA1d/k94HwREXf9SFUtVtXVQL57PJMgLjn5KF/P3ygOk8wZY7zhZeJpB6wPeF3grgtaRlVLgF1Aywj7KjBeRGaJyI2hTi4iN4pInojkbd26tVYVSSWBVwG1uWrvalMqGGNqyMvEE6yho+pHXagy4fY9W1V7AgOBW0Tk3GAnV9UXVDVXVXNbt7apkMsF+4dt26R20yMkKmtpMyYxeZl4CoDA4YPbAxtDlRGRLKApsD3cvqpa/nsL8CEp1gT38tTVnh4/8Iqnaf06AFxzxjGentMYYwJ5mXhmAl1EpKOI1MXpLDC6SpnRwLXu8hXA5+p02xgNDHF7vXUEugAzRKShiDQGEJGGwEXAQg/rEHd//WRx5EIxUr9uJmuGD+LWfl3ids5YuKr30X6HYMJoWDfT7xBMgvPsDq2qlojIrcA4IBN4RVUXiciDQJ6qjgZeBt4QkXycK50h7r6LRGQUsBgoAW5R1VIRaQt86PQ/IAt4W1XHelUHE1x1Jng797jWTFke23tsGVG2ofnd5TtdZdfJTJneV8YbnnYNUtVPgU+rrHsgYLkIuDLEvg8DD1dZtwroEftI04cE3PmoaeeCsztHPyp0tyMaxzzxRCsr056PNiYR2V9mGqtpp7ZfnN0x6rJ/6N+1hmcJrX1zu5JJZKnykKPxjiWeBLJx537PzxGLhyozom3rAup4cNXxqz7RJz5jTOKxxJNAbnjN+xGAUqGLsTWhVd9VvTtELmRMnNhfcIKYvW4HSzbt9jsMk4IyMwS/v3Kc0qEZEH3HEJPaLPEkiB8/O83vEJLCsEu7+x1C0mnVqC6NsuPXxTnYHZ5b+3YGoHG9OnGLwyQuSzxpRiSwV1vy3QS+rhodG8whd17YlbsuPM6385e/7Vo2ssn5jCWetJZ8acfUVP26mfz2/Pg8KHxEk3oAtG58aCim8sRzTMCzVe/eeEZc4jGJxxKPMSameh7THIDf9ut82DblUHNptyObsGb4IAaccEQ8wzMJwMaW99H4Rd9RWqY8GMdhcmrruZ/19O3cjW0qhITVuF4WhUUlwKEHkzMCmnUDH1y+7uyOlZpM//XTU9lbXMKu/Qd5e/o6np+yiod+eCJ/Hr2I0jK7Lg/Urll9v0OICftL9tGNb8yK+zlr+xyPnzeHy79Jm+o5t4t3o7NnZQglZcpRTeuzrKiw0jYRGHTSkWTXCd+wUiczg2YN6tKsQV1+378rXdo25sentuNnZxzD1sJiTnt4omfxG39YU1uaCfzmGa+bPL/5QaeYHKdFQ7sxXV33DTqeh390UsyPe447bNItfQ9vTgs04uqe/PP/TuGEdk0AuPr08COh18nM4IrP6TufAAAUfklEQVRe7SseUg68T2RShyUe47lzu0Q/tls4Dw4+ISbHSVYz7j2/2vtc1uMo6mbF/s88K9NJDOVXM5GupNs0rsea4YO4sHvbmMdiko8lHuO5o2LULp3Oz4Bce+YxtGlcr1rj5K0ZPog2bg+zeLr5vE70aN+UQScdGZPjdbPZbiukyojrdo/HJ1sLi305b6Wpr2vQ1laT2UqPaZkafyx+eeW6XPq492m6tG1Uq2N1btOIOpkZ7C0uYd32fTU+Tqb7Rgpsuv1D/66UlCodWjTg41vPqVWcgT67vQ8d7/k0csE0MHRgN79DiAm74vFJbf7o/dS5TfW/fUoMRiYd8VP/etP5pWOrhsy67wL6dWtbMdjqlb3a84f+XVn61wFB93nsipPDHnPinT/gs9v7MOXuvpXW18kUHhx8QtRD2vztxydx3Vk5XH3G0dTJFO688Dhu6duZ2y+I/bNCsXj/pIpObWr3xSNR2BVPmul+ZBO/Q6iRU45u5ncIcZGZIbx2fW9ObNeE7KxM6leZzTMrMyPsDf3/y+3Ai1NWsWLLnojnuum8TqzYXMjEJVuom5nBz8/M4cmJK9i+9wBT/9iXXfsPMujpqYDz7M3iTbvZvvcgE5dspk2Tegy7zLnntuLhi2tRY5OOLPH4Jv7PJwwd2I0f92zHXf+d50RQzRAa+DSlce+OLVLm+YVQnhpyChcc35aG1XhWadSvz+SOkXO48dxjGfa/xXR2vw1/cPNZ7Nx3MOL+fxzQjZLSMjrf+1nFvEnv33QWXy7bQvvmDWjfHF69/jS27C7iJ6c5040fLC1jnw+zi865/0JO/euEuJ830WSmyNWfJR6fzF2/y9Pjf/Lbc7jkX1MrrTuubSPfmi2m/+l8Tn9kUo32Hfkrb4ZWWf7QQCYu2czNb8325PjReuyKkxl8Srtq79e7Ywum3eP0dOvXrS3NGzqdLxrXqxN1R4yszAzWDB9U8bpjq4Z0bHWoA0Pfrm0qla+TmUHT+vFvoW9uXekBDrsCTlZ2j8cnu/ZH/kZaG8F6AtV2TNBHavE8SNsa9q76cc921Zp4rqqnhpxS6fWKhwfyi7M7svAv/amblVExXL9f8u67gP/Lrf1cOUe3bJDWvf5McrHE45OXv1rl6fGDTZZW28TTy4eRAx69PPzN8kgu63FUxfKs+y6gTmYGD1zanUZuk1asunrXxNK/DqBVI3tAMlqLH+zvdwgmRqypzSd7fWgnr6q6iah98/h/SNd26mwRYdUjF1NSpiEfpPz0tj5c/PRXtTpPdQU2b5noNKhrH1epwq54UtAL1/SKqlx1n+Op7f2hBy7xZxK3jAwJ+/R+96Pi29MvVFdoE9mE353rdwjVlpkhfHDzWbx5w+m8et1pXH360RXbbgsygnedzOB/Z6k0cod9hYizwqKD7Njr3f2d03Kac1GIYeb9Huf3+rNzqjUS90e3nO1hNJXdf0l3/urxKOFf/P48clo19PQcqa5L28ac07kVU/O3+R1KRN/ecz5HND383mbfbm0qjZ/X/agmHChVbntnDgCX92zPyJnrD9vvzGNbehdsnFniibPL/z2N5ZsjP2NRE6v/dnGNr0r6n9CWcYs2h9z+8I9OrGlYFUSEZg3qBO3qO/v+C+npdpcdeOIRXNGrfVxv/N9wTkeenLi8Ymj/WHv8yh6WdGLkzV+ezs9emp5QyefPl3bn5PZN2bCziH7d2lTcQ4zGgBOdoYX6dG5FmSoNs7No0zibMzq1pEPzBuw/WEph0UG6tE2doYMs8cSZV0kHIjeFhZvq+sim4e/fxGqyrul/Op9b3prDxCWVk1yLhnVZ/TfnQUS/unwvGNafnKFjQm6f+se+PP/lKt74dm3Ux3z26p7kHtPclzHTUtmbvzydOet28KNnp1V73/9cfxrXvTqz1jG0alSXoQOP50entiPT7XnZK/zg22EFdhm/86KutQ0voVniSULP/awnv3kzvs+etIxR76vsrExeuja34vXgEV9zVienCSERhkZZ/tBAXvxqFX8ft+ywbe2bN+Cui46LmHgu79meS3ocyQlHNrGE46FTj27OtKH9+M+0NbwwJXIv0Q4t6vPV3f0Apxt77kPVm+enVaO6nNiuKXf370bXIxpXJBtTfZZ44mR9jMZmW/XIxWRkCGuGD6r07Tz/4YGHlT33uNZMWb614nX59c5t53fh6Ukroj7n+zedWeN4I/k4jvdxolE3yxmS5sZzj+WFKauYt34nPTo0q+iW3axBXf7245NYvW0v9wzslhDJMp0d1aw+f7r4eP44oBvrt++jft1Mln1XyJvfrmX84s38tl9nNu4s4rbzO3NMy0NNna0aZbNm+KCKwVJ37DvANyu/5+gWDTiiaT2yszLp0aEp2Vmp8cBmorHEEyd9Hpsck+OEepgy2HM7T/3kFD5duIl7P1xYaf31Z+Xw2rQ1lSZoO6ld05Dn7HVMixpGm7zqhBkT7areRwddb/yTmSEV99DaNqnHucdFN+tqw+wsjnfHLzyrU2zmjTKRWXfqGNpd5MwZP2z0orD3U2qqeYPqPZnevGHdoDM+Nm9Yl3l/vohTjz70QOigkw/NndK3q/NH+/CPTmTa0H41jNYYY4Lz9IpHRAYATwGZwEuqOrzK9mzgdaAX8D3wE1Vd4267B7gBKAVuU9Vx0RzTT79+fRbfrPoegP9MW8OIn/Zk2sptvDV9XUyOf8JRla9Kvr3nfN6ZsY5zj6v9N7V6dTLtoUZjTFx4lnhEJBMYAVwIFAAzRWS0qgY+LHEDsENVO4vIEOBR4Cci0h0YApwAHAVMFJHj3H0iHdMX3+0qqkg65W5529sOAEc0rcfvLjwuYrk+XVrx1YptHJdC3TGNMcnLyyue3kC+qq4CEJGRwGAgMEkMBoa5y+8Bz4hzt3YwMFJVi4HVIpLvHo8ojhkXb3y7lvs/Whi5YAyUP/tyaY+aTSX8xg2nc6CkLOzT+8YYEy9eJp52QODjtwXA6aHKqGqJiOwCWrrrv62yb/m48ZGOGdSstTv44/vzKVMFhTJ1BowpU6WszClTpuqsVyhT57mX8jLq7uPVA4bB3NK3E6u27uXZq3tSWqZBOxBEy5KOMSZReJl4gnW/qnrHPVSZUOuDfXoGvYsvIjcCNwIcffTRNMzOpGvbxiCQIc5M8Rnucvm6DHHmkM/IcEIr3y4Bv2eu2c7CDbtDVLn6GtTNZPqfzidDhBP+PI5jWjbgD/270q9bm0qDImaFGL/JGGOSjZeJpwAInGikPbAxRJkCEckCmgLbI+wb6ZgAqOoLwAsAubm52u2IJoy4umfNahIndnPfGJMOvGx/mQl0EZGOIlIXp7PA6CplRgPXustXAJ+r0w95NDBERLJFpCPQBZgR5TGNMcYkMM+ueNx7NrcC43C6Pr+iqotE5EEgT1VHAy8Db7idB7bjJBLccqNwOg2UALeoailAsGN6VQdjjDGxJ1486JhocnNzNS8vz+8wjDEmqYjILFXNjVyyeqyrkzHGmLiyxGOMMSauLPEYY4yJK0s8xhhj4soSjzHGmLhKi15tIlIIHD6lZOpoBSTOBPSxlcp1A6tfskv1+nVV1ZiPLpwuE8Et86JLYKIQkbxUrV8q1w2sfskuHernxXGtqc0YY0xcWeIxxhgTV+mSeF7wOwCPpXL9UrluYPVLdla/GkiLzgXGGGMSR7pc8RhjjEkQKZ14RGSAiCwTkXwRGep3POGIyCsiskVEFgasayEiE0Rkhfu7ubteRORpt17zRaRnwD7XuuVXiMi1Aet7icgCd5+n3SnG41W3DiIyWUSWiMgiEbk9xepXT0RmiMg8t35/cdd3FJHpbqzvulN54E738a4b63QRyQk41j3u+mUi0j9gve/vZRHJFJE5IvKJ+zpl6icia9z3z9zynlyp8v50z99MRN4TkaXu3+GZvtZPVVPyB2fahJXAsUBdYB7Q3e+4wsR7LtATWBiw7jFgqLs8FHjUXb4Y+AxnptYzgOnu+hbAKvd3c3e5ubttBnCmu89nwMA41u1IoKe73BhYDnRPofoJ0MhdrgNMd+MeBQxx1z8H3OQu3ww85y4PAd51l7u779NsoKP7/s1MlPcycCfwNvCJ+zpl6gesAVpVWZcS70/3/K8Bv3SX6wLN/KxfXN+4cf6HPhMYF/D6HuAev+OKEHMOlRPPMuBId/lInOeRAJ4HrqpaDrgKeD5g/fPuuiOBpQHrK5XzoZ4fAxemYv2ABsBs4HScBwuzqr4fceaTOtNdznLLSdX3aHm5RHgv48z2OwnoB3zixptK9VvD4YknJd6fQBNgNe49/USoXyo3tbUD1ge8LnDXJZO2qroJwP3dxl0fqm7h1hcEWR93brPLqThXBSlTP7cZai6wBZiA8w1+p6qWBImpoh7u9l1AS6pf73h6ErgbKHNftyS16qfAeBGZJSI3uutS5f15LLAVeNVtKn1JRBriY/1SOfEEa2NMlS58oepW3fVxJSKNgPeBO1R1d7iiQdYldP1UtVRVT8G5MugNHB8mpqSqn4hcAmxR1VmBq4MUTcr6uc5W1Z7AQOAWETk3TNlkq18WTjP+v1X1VGAvTtNaKJ7XL5UTTwHQIeB1e2CjT7HU1GYRORLA/b3FXR+qbuHWtw+yPm5EpA5O0nlLVT9wV6dM/cqp6k7gC5y28WYiUj4sVWBMFfVwtzfFmfq9uvWOl7OBy0RkDTASp7ntSVKnfqjqRvf3FuBDnC8PqfL+LAAKVHW6+/o9nETkX/3i2Y4azx+cLL8K5yZm+Q3LE/yOK0LMOVS+x/N3Kt/8e8xdHkTlm38z3PUtcNpym7s/q4EW7raZbtnym38Xx7FeArwOPFllfarUrzXQzF2uD3wFXAL8l8o33292l2+h8s33Ue7yCVS++b4K58Z7wryXgfM41LkgJeoHNAQaByxPAwakyvvTPf9XOAN+Agxz6+Zb/eL+xo3zP/bFOD2oVgL3+h1PhFjfATYBB3G+QdyA0y4+CVjh/i7/TxZghFuvBUBuwHF+AeS7P9cHrM8FFrr7PEOVG40e1+0cnEvv+cBc9+fiFKrfycAct34LgQfc9cfi9PbJx/mQznbX13Nf57vbjw041r1uHZYR0DMoUd7LVE48KVE/tx7z3J9F5edPlfene/5TgDz3PfoRTuLwrX42coExxpi4SuV7PMYYYxKQJR5jjDFxZYnHGGNMXFniMcYYE1eWeIwxxsSVJR6TUETkCRG5I+D1OBF5KeD14yJyZ4zPuSeWx3OPeYqIXBzwepiI/D6K/UREPheRJgHrfiQiKiLdPIgzR0R+GuvjBhz/VhG53qvjm+RkicckmmnAWQAikgG0wnnwsNxZwNc+xFVdp+A8m1JdFwPztPKQQlcBU3Eexoy1HCBo4gkYlaA2XgFui8FxTAqxxGMSzde4iQcn4SwECkWkuYhk44yBNkdEGonIJBGZ7c4DMhhARB4VkZvLD+ZeadzlLv9BRGa6c4z8JdjJg5VxrwqWiMiL4sy3M15E6rvbTnPLfiMifxeRheLMS/Mg8BNx5nf5iXv47iLyhYisEpFQH8ZX44zeXR5PI5wha24gIPGIyHnuscrnWHmrfA4UEbnYXTfVnRulfP6cH7jxzHUHi2wMDAf6uOt+JyLXich/ReR/OINmSkC9FpTXxT3/lyIySkSWi8hwEblanHmJFohIJwBV3QesEZHekf7jTRrx6wlo+7GfUD84Q9QfDfwa+A3wV5wrgbOBKW6ZLKCJu9wK50lqwRn5+suAYy12j3URzvzxgvOF6xPgXLfMHvd30DI4VwUlwCluuVHAz9zlhcBZ7vJw3CGPgOuAZwLiGIZzNZftxvs9UCdI3dfiDt/ivv4Z8LK7PI1D8xqdhzPqc3s31m9wRoiohzOCcEe33DscGmngfziDYQI0cv8NzyvfHhB3AYeeYr8cZ7TtTKAtsA5nGPzzgJ3ucjawAfiLu8/tBAyPhDNawV1+v6/sJ3F+7IrHJKLyq56zcD5Qvwl4Pc0tI8AjIjIfmIgzDHtbVZ0DtBGRo0SkB7BDVdfhJJWLcIa2mQ10A7pUOW+4MqtVda67PAvIEZFmOEmiPKa3I9RrjKoWq+o2nAEZ2wYp00JVCwNeX4UzMCfu76sCts1Q1QJVLcMZhijHjXmVqq52y7wTUP5r4J/u1VYzPTSlQVUTVHW7u3wO8I46o29vBr4ETnO3zVTVTapajDNUynh3/QI3lnJbgKNCnMukoVi04RoTa+X3eU7CuaJYD9wF7Ma5ZwBOk1RroJeqHnRHTq7nbnsPuAI4gkMf2gL8TVWfD3PeoGXEmUOoOGBVKc5goNWdvrjqMYL9/ZWISIaqlolIS5yRoE8UEcW56lARuTvM8ULGpKrDRWQMztXjtyJyQYiiewOWw9Ux8PxlAa/LqFy3esD+MMcxacaueEwi+hpndOft7jft7ThT9Z6Jc/UDzlD7W9yk0xc4JmD/kTj3Q67ASULgzHb5C/eeCSLSTkTaUFk0ZSqo6g6c+09nuKsCb/4X4kzzXV3LcAatxI3/dVU9RlVzVLUDzojA54TZfylwrJssAcrvLyEinVR1gao+ijNgZLco4pyCc68qU0Ra4zQ9zqhmnY7D+QJhDGCJxySmBTj3Qb6tsm6X20wF8BaQKyJ5OFc/S8sLquoinA/TDXpohsXxOE1h34jIApyEVOkDN5oyQdwAvCAi3+BcHexy10/G6UwQ2LkgGmNw7p+A06z2YZXt7xOiF5pbh/3AzcBYEZkKbA6I6Q63k8A8nCuQz3BGKy4RkXki8rsgh/zQLTMP+By4W1W/q0Z9wLk3N7Ga+5gUZqNTG1MLItJIVfe4y0Nx5rC/vRbHOxLnKufC2sbk9nIbAaxQ1SdqerzaEJFTgTtV9Ro/zm8Sk13xGFM7g9yrmoVAH+Ch2hzMvUJ7UQIeIK2BX4nIXJy5ZZoC4e5rea0VcL+P5zcJyK54jDHGxJVd8RhjjIkrSzzGGGPiyhKPMcaYuLLEY4wxJq4s8RhjjIkrSzzGGGPi6v8Bima3FjuEUx8AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -422,9 +434,9 @@ "photlam_trappist1 = SourceSpectrum(Empirical1D,\n", " points=wavelengths, lookup_table=flux)\n", "\n", - "# Compute the flux at Earth from the luminosity \n", - "stellar_radius = 0.121 * u.R_sun # radius of trappist-1 in solar units, Van Grootel et al. 2018\n", - "distance = 39.6 * u.lyr # distance to trappist-1 in light years from Gaia parallax\n", + "# Scale the flux to get the value at Earth\n", + "stellar_radius = 0.117 * u.R_sun # radius of trappist-1 in solar units\n", + "distance = 12.1 * u.pc # distance to trappist-1 in parsecs\n", "photlam_trappist1 = photlam_trappist1 * float(stellar_radius / distance) ** 2 / np.pi\n", "\n", "photlam_trappist1.plot(flux_unit='Jy', left=0, right=60000)" @@ -439,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -447,7 +459,7 @@ "output_type": "stream", "text": [ "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "256581 cts\n" + "311256 cts\n" ] } ], @@ -474,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -492,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -517,22 +529,22 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 130, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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16nHmzBmWLFnChAkTNFnkEq1hKKXyhG+++Ybw8HB+/fVXOnfuzPvvv0/x4sWdDitf8Ykahg4NopTv+vPPP3n11Vd57LHHKFKkCDExMYwZM0aThQNsJQwReVZEGqd6/T8ROSgiS0Xkds+FZ48ODaKUb5o9ezb+/v5MmjSJXr16sX37durVq+d0WPmW3RpGv5RfRKQ68F9gNFAIGO7+sJRS+dnvv/9Oq1at+M9//kOZMmXYtGkT77//vs4f7zC7CeMuYLfr96eBr40xHwJvAo08EZhSKv8xxjBx4kT8/f1ZsGABgwcPZuPGjVSrVs3p0BT2G73PASk3DBsBn7t+T0xVrpRS2fbLL7/Qvn17li5dSr169YiKiuK+++5zOiyVit0axhpguIj0AYKBRa7ye4EDnghMKZU/JCcnM3bsWCpXrszatWsZO3Ys3377rSYLL2Q3YXQBLgCtgA7GmMOu8qbAUk8EppTyfT/88AMPPfQQXbt2pV69esTFxdG5c2cKFPCJBzh9jq1bUsaYg0CLdMq7uT0ipZTPS0pKYujQofTv359ixYoxceJEXnrpJUTE6dDUNdh9rHafiJRKp/xGEdnn/rCUUr5q27Zt1KpVi969e/PEE0+wa9cu2rZtq8kiD7Bb7ysPFEyn/DqgnNuiUUr5rHPnztGrVy9q1qzJ77//zuzZs5kxYwa33Xab06Epm655S0pEWqZ62UxEUnelLoj1xNTPnghMKeU7vvvuO0JCQvjxxx9p164dw4YN46abbnI6LJVFmbVhzHT9NEB0mnVJwH6gu5tjUkr5iJMnT9KrVy8iIiIoX748y5Yt45FHHnE6LJVN10wYxpgCACLyM1DTGHMsV6JSSuV5S5YsoX379hw4cIDXX3+dgQMHcsMNNzgdlsoBu09J6TyHSilbjh8/zptvvskXX3yBn58fa9eupU6dOk6HpdzA9vDmIlIbq83iVtI0lhtjXnNzXEqpPMYYw6xZs+jcuTMnTpygT58+9O7dm+uuu87p0JSb2EoYIvIW8CGwBziM1aaRwqS7k1Iq3/jtt9/o3Lkzc+bMoUaNGnzzzTdUrVrV6bCUm9mtYbwOvGaMGevJYFITkfrAC1gx+htj6ubWuZVS9hhjGD9+PG+++Sbnz5/nww8/5I033uD//k/nZvNFdvthlOCf8aOyTUQ+F5EjIhKXpryJiOwWkT0i0hPAGLPGGNMBWABMzOm5lVLu9fPPP9O4cWNCQkKoWrUqO3bsoEePHposfJjdhDEVaOKG801IexwRKQhEYI1L5Q+0FhH/VJu0cZ1fKeUFLl26xEcffURAQAAbNmzgk08+YdWqVdx7771Oh6Y8zO5/BQ4A/UXkQSAWqw/GZcaYEXYOYoyJEZHyaYprAXuMMfsARGQa8CSQICJ3AonGmL9txqmU8qCEhARCQ0NZv349jz/+OJGRkdxxxx1Oh6Vyid2EEQqcAuq6ltQMYCthZKAsVw6RfhCo7fo9BBh/rZ1FJBwIB7jzzjtzEIZSKiMXLlzgww8/ZMCAARQvXpzJkyfTpk0bHf8pn/GGfhjp/YszrvP2zWxnY8w4YBxAcHCwPrGllJtt3ryZkJAQYmNjef755/noo4+49dZbnQ5LOcAbBp0/CKSu05bDenRXKeWgs2fP8vbbb1O7dm2OHTvG3LlzmTp1qiaLfMxuP4zR11qfw457m4B7RKQCcAh4Hquh2zYRaQG0qFSpUg7CUEql+PbbbwkNDWXPnj2EhYXx4YcfcuONNzodlnKY3RpGlTRLdawv9bZAgN2TichUYD1wn4gcFJEQY8xFrBn9lgK7gOnGmHj7bwGMMfONMeElS5bMym5KqTT+/vtvOnbsyMMPP0xycjIrVqxg3LhxmiwUYL8No2HaMhEpgjWC7Rq7JzPGtM6gfBFu6OehlMq+hQsX0qFDBw4fPsybb77JgAEDKFq0qNNhKS+S7TYMY8w5YBDQ233hKKVy27Fjx3jxxRdp3rw5JUuWZN26dQwfPlyThbpKThu9SwOOj1csIi1EZFxiYmLmGyulAGtYj2nTpuHn58f06dPp27cvW7dupXbt2pnvrPIlu43eb6YtAm7HGuvJ8VtJxpj5wPzg4OAwp2NRKi84dOgQnTp1Yt68edSsWZPo6GiqVKnidFjKy9ntuNc1zetk4ChWp7rBbo1IKeUxxhiioqJ46623SEpKYvjw4bz++usULFjQ6dBUHuANHfeUUrlg7969hIWFsWrVKho2bMhnn31GxYoVnQ5L5SFZbsMQkRtEpJgngskubcNQKmOXLl1ixIgRVKlShS1btjBu3DhWrFihyUJlme2EISKdReRXIBH4W0R+EZFOngvNPu2HoVT64uLiqFu3Lt27d+eRRx4hISGBsLAwHQNKZYuthCEi/wWGYPW7aOxaxgNDUuavUEp5jwsXLtC/f3+qV6/Ovn37mDp1KnPnzqVs2bJOh6byMLuN3h2AcGNM6nkpVojIT8D7WMlEKeUFNm7cSEhICHFxcbzwwguMGjWKW265xemwlA+we0vqVqwxn9LaCNzmvnCyR9swlIIzZ87QvXt36tSpw19//cWCBQuYPHmyJgvlNnYTxo+kPyBgG2C3+8LJHm3DUPndqlWrqFKlCiNGjCA8PJz4+HiaNWvmdFjKx9i9JdUPmC4iDwFrsearqAc0AJ7xTGhKqcwkJibSo0cPPvvsMypVqsTq1atp0KCB02EpH2WrhmGMmY01C97vQHPgCdfvtYwxX3suPKVURubPn4+/vz/R0dG89dZb7NixQ5OF8ii7NQyMMVuAFz0Yi1LKhqNHj/Laa68xbdo0qlSpwty5cwkODnY6LJUP2H2s9hkReTKd8idFpJX7w8oabfRW+YExhi+//BI/Pz9mz57NgAED2Lx5syYLlWvsNnr3A86lU37atc5R2uitfN2BAwdo0aIFL7zwAvfccw/btm3j3XffpXDhwk6HpvIRuwnjbtJ/GmqPa51SygOSk5OJjIykcuXKrFq1ilGjRvHdd9/h7+/vdGgqH7LbhvEncA+wP035vcBJdwaklLL89NNPhIWF8e2339KoUSPGjRvH3Xfr/8+Uc+zWMOYCI0Xk3pQCEbkPGAHoU1JKudHFixcZOnQogYGBbN++nejoaJYtW6bJQjnObg3jbWAJkCAiv7nKbsfq6d3DE4EplR/FxsYSEhLC5s2beeqpp4iIiOBf//qX02EpBdifD+Mk8KCIPAoEYc24txVYYYwxHoxPqXzh/PnzDBo0iMGDB3PzzTczffp0WrVqpaPKKq9iux8GgDFmGbDMQ7Fkm4i0AFpUqlTJ6VCUyrL169cTEhLCrl27aNu2LSNGjKBUqVJOh6XUVbI8gZI30sdqVV50+vRpunXrxoMPPsipU6dYtGgREydO1GShvFaWahhKKfdYvnw5YWFh7N+/n86dOzN48GCKFy/udFhKXZNP1DCUyiv++usvQkJCePTRRylUqBAxMTGMHTtWk4XKEzRhKJVLvv76a/z9/Zk4cSI9e/Zkx44d1K9f3+mwlLLN7lhS+0TkqhurInKjiOxzf1hK+Y4//viDZ599lqeffprbbruNjRs3MnjwYK6//nqnQ1MqS+zWMMoDBdMpvw7QSYKVSocxhi+++AI/Pz/mzp3LoEGD2LhxI9WrV3c6NKWy5ZqN3iLSMtXLZiKSejjYgkAjrh4uRKl879dff6V9+/YsWbKEunXrEh0dzf333+90WErlSGZPSc10/TRAdJp1SVjJorubY8oy7YehvEVycjKffPIJPXv2xBjD6NGj6dy5MwUKaHOhyvuu+a/YGFPAGFMA+BW4NeW1a7nOGHOfMWZB7oR6zTi1H4Zy3O7du2nQoAFdunShbt26xMXF0bVrV00WymfYnaK1gjHmmKeDUSovunjxIkOGDKFq1arEx8czYcIElixZQvny5Z0OTSm3sv1fHxFpJiIxInJMRI6KyLci8rgng1PK223fvp3atWvTq1cvmjdvTkJCAi+//LKOAaV8kt3HakOBOcBe4B2gJ/AzMEdE2nkuPKW807lz5+jduzfBwcEcOnSImTNnMnPmTMqUKeN0aEp5jN2hQd4B3jTGjE1VFi0iW7CSx+duj0wpL7V27VpCQkLYvXs3r7zyCsOHD+fmm292OiylPM7uLak7sebDSGsxcJf7wlHKe506dYrXXnuN+vXrc+7cOZYuXcr48eM1Wah8w27C+BV4NJ3yxsAv7gtHKe/0zTffEBAQwNixY+natStxcXE0btzY6bCUylV2b0kNA8aISHVgHVa/jHrAS0BXD8WmlONOnDhB9+7dmTBhAvfffz9r1qzhwQcfdDospRxhd8a9T0XkCFYnvZTe37uAZ40xcz0VnFJOmjVrFp07d+bYsWP07t2bd999lyJFijgdllKOsT0fhjFmDtaTUkr5tN9++40uXbowe/ZsqlWrxpIlSwgKCnI6LKUcl+UuqK4Ram9OvXgisCzG1EJExiUmJma+sVIZMMYwYcIE/P39WbhwIUOGDGHjxo2aLJRysdsP4y4RWSwi54DjwFHXcsz101E6NIjKqf379/PYY4/x6quvEhAQwI4dO3jnnXf4v//TSSmVSmH3r2E8cCPQDjiM1eitVJ6XnJxMREQEvXr1QkSIiIigQ4cOOv6TUumwmzBqAQ8YY+I8GYxSuWnXrl2Ehoaybt06mjRpwqeffsqdd97pdFhKeS27/436GWuyJKXyvKSkJAYNGkRQUBA//PADX3zxBYsWLdJkoVQm7CaM14HBIqITTqg8bevWrdSsWZN3332Xp556ioSEBF566SUdLFApGzK8JSUiJ7myraIIsFtEzgMXU29rjCnhmfCUco+zZ8/Sv39/hg0bRunSpZkzZw5PPfWU02Epladcqw2jS65FoZQHrVmzhtDQUH788UdCQkIYOnQoN910k9NhKZXnZJgwjDETczMQpdzt5MmT9OzZk48//pgKFSqwfPlyGjVq5HRYSuVZth8yF5EiQBvA31WUAEw1xpz1RGBK5cTixYtp3749Bw8epFu3bgwcOJBixYo5HZZSeZrdjnvVsZ6UGo71iG0trAEJ97nWKeUVjh8/Ttu2bXn88ccpXrw469atY+TIkZoslHIDu09JjQPWAOWMMQ8ZYx4C7gBiXOuUcpQxhunTp+Pn58fUqVPp06cPW7du5YEHHnA6NKV8ht1bUpWBtsaY0ykFxpjTIvIesNkjkSll0+HDh+ncuTNff/01NWrUYPny5QQGBjodllI+x24N4wfgX+mU3w786L5wlLLPGEN0dDT+/v4sWbKEoUOH8v3332uyUMpD7NYw3gVGu2oU37vKHnCV90w9Yq0x5oR7Q1Tqavv27SM8PJwVK1bQoEEDoqKiqFRJ+5Uq5Ul2E8Z8188v+aczX0rX2LmpXhugoHtCU+pqly5dYsyYMfTu3ZuCBQsSGRlJWFiYDhaoVC6wmzAaejSKdIhIAWAAUALYrP1CVHx8PCEhIWzYsIFmzZoRGRlJuXLlnA5LqXzD7hSt37rjZCLyOdAcOGKMCUhV3gT4CKt2EmWMGQI8CZQFTgAH3XF+lTdduHCBDz74gAEDBlCiRAmmTJlC69atdfwnpXJZVjruFQWCgFtJ01hujJlt8zATgLHAF6mOWxCIAB7FSgybRGQecB+w3jWf+Exghd1Yle/YtGkTISEh7Ny5k+eff57Ro0dTunRpp8NSKl+ylTBE5BFgKlAqndW22y2MMTEiUj5NcS1gjzFmn+tc07BqFweAC65tLtk5vvIdZ86coV+/fgwfPpwyZcowd+5cnnjiCafDUipfs9tS+BGwEKvjXoE0S04buctiJYcUB11ls4HHRGQMVgfBdIlIuIhsFpHNR486PluscoPVq1dTtWpVhg4dSmhoKAkJCZoslPICdm9JlQeeMMYc9kAM6d2INsaYM0BIZjsbY8bh6m0eHBysU8fmYYmJibzzzjt8+umnVKxYkZUrV9KwYa4/b6GUyoDdGsZarDYFTziINcxIinJY84arfGThwoVUrlyZzz77jO7duxMbG6vJQikvY7eGEQkME5F/ATuBpNQrjTFbcxDDJuAeEakAHAKexxoV1zYRaQG00I5bec/Ro0fp1q0bX375JQEBAcyePZtatWo5HZZSKh12PraHAAAT30lEQVRiTOZ3cUQk+Rqrjd12DBGZCjwM3AL8AfQ1xkSLyOPAKKzG88+NMYPsHC+t4OBgs3mzDm2VFxhj+Oqrr+jatSuJiYn07t2bXr16UbhwYadDUyrfEZEtxpjgzLazW8OokMN4ADDGtM6gfBGwyB3nUN7v0KFDdOzYkfnz51OrVi2io6MJCAjIfEellKPsJowzxhh9BEnlSHJyMlFRUfTo0YOkpCRGjBjBa6+9RsGCOpqMUnmB3UbvQyIyS0Saihd2rxWRFiIyLjEx0elQVAb27NlDo0aNaN++PTVq1GDnzp288cYbmiyUykPsJoxmwHlgFnBARAaISEXPhZU1xpj5xpjwkiVLOh2KSuPSpUsMHz6cwMBAtm7dymeffcaKFSuoWNFr/vkopWyylTCMMcuMMW2w5sQYDDQFfhSRlSLygmu+b6WuEBcXR506dXjrrbd45JFHSEhIIDQ0VMeAUiqPytKY0MaYv4wxEa7W9NeAusAk4LCIDBGRGzwRZGb0lpR3uXDhAv369aN69ers37+fadOmMXfuXMqWLet0aEqpHMhSwhCR20Wkp4jsBj4ApgENgI5AE+Br94eYOb0l5T02bNhA9erV6d+/P8899xwJCQk899xzWqtQygfYHXywJdAOaAzEYY0tNdkY83eqbXYC2z0RpPJ+p0+fpk+fPowaNYqyZcuyYMECmjVr5nRYSik3svtY7Xis0WrrGGO2ZLDNz0C2OtypvG3lypWEhYWxb98+OnbsyJAhQyhRooTTYSml3MxuwrjdNRhghowxZ4H+OQ9J5RV//fUXPXr0uDyf9urVq2nQoIHTYSmlPMRuG0Z5Ebk8+KCIPCoik0Wkl2sCJEdpo3fumzdvHpUrV+bzzz/n7bffJjY2VpOFUj7ObsKIBqoBiEg5YC5wM9AZGOiZ0OzTRu/cc+TIEZ5//nmefPJJbrnlFjZs2MAHH3zA9ddf73RoSikPs5sw/ICUEWmfATYYYx4HXgLSHR9K+RZjDFOmTMHf3585c+YwYMAANm/eTHBwpuOVKaV8hN02jIL8M11qI/4ZKHAvcJu7g1Le5cCBA3To0IFFixbxwAMPEB0djb+/v9NhKaVymd0aRhzQUUTqYyWMJa7yssAxTwSmnJecnMwnn3xC5cqVWb16NaNGjeK7777TZKFUPmU3YbwDhAGrganGmJ2u8ieAjR6IK0u00dv9fvrpJxo2bEinTp2oXbs2cXFxvP766zpYoFL5mN2xpGKA0sAtxph2qVZ9itXL21Ha6O0+Fy9e5MMPPyQwMJDY2Fg+//xzvvnmGypUcMuUKEqpPMxuGwbGmEvAn2nK9rs7IOWcHTt2EBISwpYtW3j66aeJiIjg9ttvdzospZSXyNJYUso3nT9/nj59+hAcHMyBAweYMWMGs2bN0mShlLqC7RqG8k3r168nJCSEXbt20bZtW0aMGEGpUqWcDksp5YW0hpFPnTp1im7duvHggw9y+vRpFi9ezMSJEzVZKKUy5BMJQ5+Sypply5ZRpUoVPvroIzp16kRcXBxNmjRxOiyllJfziYShT0nZ8+effxISEkLjxo0pXLgwMTExjB07luLFizsdmlIqD/CJhKEyN2fOHPz9/Zk4cSK9evVix44d1K9f3+mwlFJ5iCYMp+zdC506QYkSUKCA9bNTJ6vcjX7//XeeeeYZWrZsSZkyZdi4cSPvv/8+RYroNOxKqazRhOGExYshMBCiouDkSTDG+hkVZZUvXpzjUxhj+OKLL/D392f+/Pm8//77bNy4kerVq7vhDSil8iNNGLlt715o1QrOnIGkpCvXJSVZ5a1a5aim8csvv9C0aVNefvll/Pz82L59O7169aJQoUI5DF4plZ9pwshtw4dfnSjSSkqCkSOzfOjk5GQiIiIICAjgu+++Y8yYMaxZs4b7778/m8EqpdQ/NGHktsmT7SWMSZOydNjdu3fToEEDunTpwoMPPkh8fDxdunShQAG9xEop99Bvk9x26pRbt0tKSmLIkCFUrVqV+Ph4JkyYwOLFi7nrrrtyEKRSSl3NJ4YGEZEWQItKlSo5HUrmbrjBauC2s10mtm3bRkhICNu2baNVq1aMGTOGMmXKuCFIpZS6mk/UMPJUx70XX4TMGp8LFYKXXspw9blz5/jvf/9LzZo1OXz4MLNmzWLGjBmaLJRSHuUTCSNP6d7dXsJ44410V61du5agoCAGDx5M27Zt2bVrFy1btvRAoEopdSVNGLmtYkWYOROKFr06cRQqZJXPnGltl8rJkyfp2rUr9evX5/z58yxdupTPP/+cm266KReDV0rlZ5ownNC0KcTGQnj4lT29w8Ot8qZNr9h86dKlBAQEEBERQdeuXdm5cyeNGzd2KHilVH7lE43eeVLFijB2rLVk4MSJE7z55ptMnDiR+++/n++++466devmYpBKKfUPrWF4qZkzZ+Ln58eUKVPo3bs327Zt02ShlHKU1jC8zG+//UaXLl2YPXs21atXZ+nSpQQFBTkdllJKaQ3DWxhjGD9+PP7+/ixcuJAhQ4awYcMGTRZKKa+hNQwvsH//fsLDw1m2bBn169cnKiqKe++91+mwlFLqClrDcNClS5cYPXo0AQEBrF+/no8//pjVq1drslBKeSWfqGHkqaFBXHbt2kVISAjr16+nadOmREZGcueddzodllJKZcgnahh5aWiQpKQkBg0aRFBQELt372bSpEksXLhQk4VSyuv5RA0jr9iyZQvt2rUjNjaWZ599ljFjxnDrrbc6HZZSStniEzUMb3f27Fl69uxJ7dq1OXr0KHPmzOGrr77SZKGUylO0huFhMTExhIaG8tNPPxEaGsrQoUO58cYbnQ5LKaWyTGsYHvL333/TqVMnGjRowMWLF1m+fDmfffaZJgulVJ6lCcMDFi1aREBAAJGRkbzxxhvs3LmTRo0aOR2WUkrliN6ScqNjx47xxhtvMHnyZPz9/Vm3bh0PPPCA02EppZRbaA3DDYwxTJ8+HX9/f6ZNm8b//vc/tm7dqslCKeVTtIaRQ4cPH6ZTp07MnTuX4OBgli9fTmBgoNNhKaWU22kNI5uMMURHR+Pv78/SpUsZNmwY69ev12ShlPJZWsPIhn379hEWFsbKlStp0KABUVFR5KVhSZRSKju0hpEFly5dYuTIkQQEBLBp0yY+/fRTVq5cqclCKZUvaA3Dpvj4eEJCQtiwYQPNmjUjMjKScuXKOR2WUkrlGq1hZOLChQu89957VKtWjb179/Lll18yf/58TRZKqXxHaxjXsGnTJkJCQti5cydt2rRh1KhRlC5d2umwlFLKEV5bwxCRh0VkjYhEisjDuXnuM2fO0KNHDx544AFOnDjBvHnzmDJliiYLpVS+lqsJQ0Q+F5EjIhKXpryJiOwWkT0i0tNVbIBTQBHgYG7FuHr1agIDAxk2bBhhYWHEx8fTokWL3Dq9Ukp5rdyuYUwAmqQuEJGCQATQFPAHWouIP7DGGNMUeAfo7+nAEhMTad++PQ0bNgRg5cqVREZGkhcmZVJKqdyQqwnDGBMDnEhTXAvYY4zZZ4y5AEwDnjTGJLvW/wlc58m4Fi9eTOXKlYmKiuKtt94iNjb2cuJQSill8YZG77LAgVSvDwK1RaQl8BhwIzA2o51FJBwIB7I9zemBAwe46aabmD17NrVq1crWMZRSytd5Q8KQdMqMMWY2MDuznY0x44BxAMHBwSY7AYSGhvLKK69QuHDh7OyulFL5gjc8JXUQuCPV63LA4dwMoECBAposlFIqE96QMDYB94hIBREpDDwPzMvKAUSkhYiMS0xM9EiASimlcv+x2qnAeuA+ETkoIiHGmItAF2ApsAuYboyJz8pxjTHzjTHh+kSTUkp5Tq62YRhjWmdQvghYlJuxKKWUyhpvuCWllFIqD/CJhKFtGEop5Xk+kTC0DUMppTzPJxKGUkopz/OGjns5JiItgBbAGRHZlWZ1SSC9e1Vpy28BjnkmwkxlFKOnj2N3+8y2u9Z6u59/RmVOXRenrklW9snudclpuf6tZH87b/1bucvWVsYYn1mAcXbK0isHNntT3LlxHLvbZ7bdtdbb/fyvUebIdXHqmuTGdclpuf6tuP+aZPW6OPW34mu3pObbLLtWuRPcFUtWj2N3+8y2u9b6rHz+ek2ytk92r4u7yp2gfyv2zuMR4spM+Z6IbDbGBDsdh7qSXhfvo9fEO+XGdfG1GkZOjHM6AJUuvS7eR6+Jd/L4ddEahlJKKVu0hqGUUsoWTRhKKaVs0YShlFLKFp/ouOcJIvIwMACIB6YZY1Y7GpBCRApgXZMSWM+cT3Q4JAWISH3gBazvE39jTF2HQ8r3ROROrKmtjwE/GmOGuOO4+aqGISKfi8gREYlLU95ERHaLyB4R6ekqNsApoAjWrIDKA7J4TZ7EmgM+Cb0mHpWV62KMWWOM6QAsADSJe0gW/1buBRYaY9oB/m4LIrd7ajq5AA8B1YG4VGUFgb3A3UBhYIfrAy7gWn8bMMXp2H11yeI16Qm0d20z0+nYfXnJynVJtX46UMLp2H11yeLfSilgFbASeNVdMeSrGoYxJgY4kaa4FrDHGLPPGHMBmAY8aYxJdq3/E7guF8PMV7JyTbBqFX+6trmUe1HmP1m8Lim3QBKNMX/nbqT5RxavyatAX2PMv4Fm7oohXyWMDJQFDqR6fRAoKyItReRTYBLWvUCVe9K9JsBs4DERGQPEOBFYPpfRdQEIAcbnekQqo2uyBHhNRCKB/e46mTZ6g6RTZowxs7G+oFTuy+ianMH6YlLOSPe6ABhj+uZyLMqS0d9KHNDK3SfTGoaVke9I9boccNihWJRFr4l30uvifXL1mmjCgE3APSJSQUQKA88D8xyOKb/Ta+Kd9Lp4n1y9JvkqYYjIVGA9cJ+IHBSREGPMRaALsBTYBUw3xsQ7GWd+otfEO+l18T7ecE108EGllFK25KsahlJKqezThKGUUsoWTRhKKaVs0YShlFLKFk0YSimlbNGEoZRSyhZNGMrricjDImJE5BanY3EXEVkgIhOcjkOprNCEoZTKkIi8IiKnnI5DeQdNGEqlQ0QKiEhBp+NQyptowlCOE5HrRGSUiPwhIudE5HsRqZfOpg+IyHbXNltEpEaqY5QUkUmuGcnOicg+EemWZv041/qTIvKtiASnWv+KiJwSkcddM5pdALqIyAURKZUm3vdFZEeq13VdxzsjIodE5BMRKZFqfVERmeA6/h8i8l+bn8sDIrJSRE6LSKKIrBCRf9n5zNK7jSci5V1lwWm2aSQiG1zxbxaR6inrsYYsL+bazohIP9e6liISKyJnReSE6/3fZud9qbxLE4byBh8CzwHtgGrATmCJiNyeZrthwDtAMLAPWCgiRV3rBgJVgObA/a5jHQIQEQEWYs0T0Nx1jhhgZZpzFAHeBdpjzVo2ATgOPJOygetYrYHJrtdVgG+wBnyrCrQEgoDP08T9KPAfoJHr/A9d6wMRkapYM6btAR4EHsCa0S5lSgK7n5kdg7FmM6zuer9TXO9zHdANOAPc7lqGiUgZrIl6JgJ+rvcyKRvnVXmN09MO6pK/F6AY1v/m26YqS5l2cqDr9cNY8y68kGqbG4C/gFDX63nA+AzO8W+s+dmvT1O+HXjb9fsrrnPUSLPNSGBNqtf1sGb7K+t6/QUQnWafINexbnXFeT6D2Cdc43OZAnzvhs/sllTblHeVBafZ5rFU2zzoKiuX6nM5leb81V3b3OX0vx9dcnfRGoZyWkWgELA2pcAYcwlrVM60k9evT7XNKaz/Vads8wnwrIjsEJFhItIg1X41gKLAUddtoVOuhtwA1/lTXMRKIqlNBh4Ukbtcr18AVhtjDqU69otpjpvyXiq6lsIZxH4t1YAVGazLymdmR2yq31PmUrj1GtvvAJYDcSIyS0Q6ikjpbJxX5TGaMJTTUmYMS2/YZNtDKRtjFgN3Yd3+uQXrdlXKlKEFgD+w/ueferkf6JPqMOddX7ypj7sF+AFoIyKFsG5PTU61SQEgKs1xqwL3YCWf9GZEs+Na+9n5zJLTbAtWkklPUjr7Z/jd4PqMGruWWKxZEH9y3UZTPkwThnLaHqzbK6kbbAsCdYCENNs+kGqbYlg1hF0pZcaYY8aYScaYV7C+xF4WkeuArcBtQLIxZk+a5YiNGKdg1SyaYN0OmpVq3VagcjrH3WOMOet6f0kZxH4tW7FupaXHzmd21PUzdZtGUCbnTM8FrNtdVzCW9caY/kBNrJrJc9k4vspDdE5v5ShjzGkR+QQYIiLHgJ+BN7C+4D9Os/m7InIU68vpf1hfZl8CiMh7WF+y8Vj/rlsC+4wx50VkOdbtm7ki8jZWjaEMVgJYboxZk0mYk4EBrmWeMebvVOs+AL4XkUjgU+AkVs2lhTGmvTHmlIhEAx+kiT2zR3aHuo47DogAzgH1gW+MMb/a+Mz2AAeAfiLSE6v94t1Mzpme/UAREXkU2IbVAB4IPII1ac8fWLfP7uDqBK98jdONKLroAlwHjML68jkPfA/US7X+YaxbJU9g3QI5j5UcaqbapjdWsjgDnAAWAX6p1hcHPsKaA/kC1pfpNKCia/0rpGncTRNjjCuGFumsCwaWAH8Dp7HaJ95Ltb4YVuP4KeAI1m2wBVyj0du1Xz3Xec9iNZIvB26385m5tqmLdVvsLFb7RjPSb/TOsGHcVfYJcMxV3g/ryajFqc69B9fDA7r49qIz7imllLJF2zCUUkrZoglDKaWULZowlFJK2aIJQymllC2aMJRSStmiCUMppZQtmjCUUkrZoglDKaWULZowlFJK2fL/Bsu5Q5BnHtMAAAAASUVORK5CYII=\n", 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" ] From 397d3a2c739924d72ceb690079a7400832b27acf Mon Sep 17 00:00:00 2001 From: tcjansen Date: Fri, 7 Jun 2019 11:48:16 -0400 Subject: [PATCH 06/26] Added preamble with links to parts of the tutorial --- observing_examples.ipynb | 205 +++++++++++++++++++++++++++++++++------ 1 file changed, 175 insertions(+), 30 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 466cdaa..4858c9a 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -4,13 +4,66 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#
Computing the count rate of observing HAT-P-11 with the APO 3.5m telescope using synphot
" + "# synphot: Predicting count rates with a ground based telescope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Authors\n", + "Tiffany Jansen, Brett Morris, Pey Lian Lim, & Erik Tollerud" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Objectives\n", + "
    \n", + "
  • Query data directly from other websites using `astropy.coordinates.Skycoord`, `astroquery.Gaia`, `astropy.io`, and `astropy.utils`
    • \n", + "
  • Construct a source spectrum using `synphot.SourceSpectrum`
    • \n", + "
  • Simulate bandpass throughput with `synphot.SpectralElement`
    • \n", + "
  • Model effects on the source spectrum such as atmospheric transmission and quantum efficiency with `synphot.SpectralElement`
    • \n", + "
  • Combine all of these effects into a simulated observation with `synphot.Observation`
    • \n", + "
  • Compute the expected count rate from this observation with `synphot`'s `countrate()` function\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Keywords\n", + "synphot, synthetic photometry, astropy, astroquery, astronomy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "In this tutorial we will:\n", + "
    \n", + "
  1. Query the properties of our target star (HAT-P-11 to start)
    2. \n", + "
  2. Download model spectra from PHOENIX
    3. \n", + "
  3. Construct a source spectrum
    4. \n", + "
  4. Create the bandpass of observation
    5. \n", + "
  5. Model attenuation by the atmosphere
    6. \n", + "
  6. Model the effect of the quantum efficiency
    7. \n", + "
  7. Combine all of the effects and \"observe\"
    8. \n", + "
  8. Compute the count rate and total counts
    9. \n", + "
  9. Another example, this time with TRAPPIST-1
    10. \n", + "
  10. Comparing simulated counts to observed counts
    11. \n", + "
" ] }, { "cell_type": "code", "execution_count": 18, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -34,20 +87,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Compute the photon flux (of HAT-P-11)" + "\n", + "## 1. Query the properties of HAT-P-11" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First we get the star's parameters using astroquery's Gaia query: " + "First we get the star's parameters using astroquery's Gaia query: " ] }, { "cell_type": "code", "execution_count": 19, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -81,13 +137,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next we are going to download a model spectrum for HAT-P-11 from PHOENIX:" + "\n", + "## 2. Download a model spectrum for HAT-P-11 from PHOENIX:" ] }, { "cell_type": "code", "execution_count": 20, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "T_eff = round(T_eff, -2) # round to nearest 100 K\n", @@ -107,13 +166,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Then construct a `synphot` source spectrum object from the above arrays:" + "\n", + "## 3. Construct a `synphot` source spectrum object from the above arrays:" ] }, { "cell_type": "code", "execution_count": 21, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -149,7 +211,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Simulate the observation with a specific instrument" + "\n", + "## Now we will simulate the observation with a specific instrument" ] }, { @@ -162,7 +225,9 @@ { "cell_type": "code", "execution_count": 22, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", @@ -170,6 +235,13 @@ "gain = 1.9 # the gain of this detector in e-/ADU" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Create the bandpass" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -181,7 +253,9 @@ { "cell_type": "code", "execution_count": 23, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -217,13 +291,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Then we model the attenuation by the atmosphere using the Cerro Paranal model transmittance curves for an airmass of 1.5:" + "\n", + "## 5. Model the attenuation by the atmosphere" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the Cerro Paranal model transmittance curves for an airmass of 1.5:" ] }, { "cell_type": "code", "execution_count": 24, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -247,13 +331,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We also model the affect of the CCD's quantum efficiency on the spectrum. For this we use the values in the table found in section 3.5 on this page." + "\n", + "## 6. Model the affect of the CCD's quantum efficiency on the spectrum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this we use the values in the table found in section 3.5 on this page." ] }, { "cell_type": "code", "execution_count": 25, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -275,6 +369,14 @@ "quantum_efficiency.plot(left=3000, right=14000)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7. Combine all of the effects and \"observe\"" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -285,7 +387,9 @@ { "cell_type": "code", "execution_count": 26, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -318,7 +422,9 @@ { "cell_type": "code", "execution_count": 27, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "band = 'r' # only want to know about the r-band\n", @@ -329,6 +435,14 @@ " force='taper')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 8. Compute the count rate and total counts" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -339,7 +453,9 @@ { "cell_type": "code", "execution_count": 28, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "waverange = (3000, 11300) # according to the ARCTIC webpage, the z band is cut off around 11k AA\n", @@ -357,7 +473,9 @@ { "cell_type": "code", "execution_count": 29, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -375,19 +493,29 @@ "print(int(counts_hatp11.value), \"cts\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Another example, this time with TRAPPIST-1" + ] + }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ - "#
Same instrument and telescope, this time observing TRAPPIST-1
" + "(using the same instrument and telescope)" ] }, { "cell_type": "code", "execution_count": 42, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", @@ -407,13 +535,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Unfortunately Gaia does not have all the parameters we need for TRAPPIST-1, so we use the values cited in Gillon 2017." + "Unfortunately Gaia does not have all the parameters we need for TRAPPIST-1, so we use the values cited in Gillon 2017:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ @@ -452,7 +581,9 @@ { "cell_type": "code", "execution_count": 44, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -481,13 +612,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### We know from observations using AGILE on APO's 3.5m telescope that the actual count values for these stars taken on {date} are:" + "\n", + "## 10. Comparing simluated counts to observed counts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know from observations using AGILE on APO's 3.5m telescope that the actual count values for these stars taken on {date} are:" ] }, { "cell_type": "code", "execution_count": 45, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -505,7 +646,9 @@ { "cell_type": "code", "execution_count": 46, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -530,7 +673,9 @@ { "cell_type": "code", "execution_count": 47, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -575,7 +720,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -589,7 +734,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.5.2" } }, "nbformat": 4, From c8fc9d5b0819175a1bb0f1c802db3ff05c9ffdd5 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Mon, 10 Jun 2019 10:26:43 -0400 Subject: [PATCH 07/26] Use pmv_kpno to simulate the atmospheric transmission --- CerroParanal-atmosx1.5.txt | 297001 ---------------------------------- observing_examples.ipynb | 232 +- 2 files changed, 149 insertions(+), 297084 deletions(-) delete mode 100644 CerroParanal-atmosx1.5.txt diff --git a/CerroParanal-atmosx1.5.txt b/CerroParanal-atmosx1.5.txt deleted file mode 100644 index 4fd4d6f..0000000 --- a/CerroParanal-atmosx1.5.txt +++ /dev/null @@ -1,297001 +0,0 @@ -300 0.00629198 -300.1 0.00650494 -300.2 0.00687728 -300.3 0.00715613 -300.4 0.00743213 -300.5 0.00766073 -300.6 0.00806075 -300.7 0.00856993 -300.8 0.00916688 -300.9 0.009882 -301 0.0105368 -301.1 0.0113514 -301.2 0.0119872 -301.3 0.0124846 -301.4 0.0130956 -301.5 0.0135419 -301.6 0.0138204 -301.7 0.0140771 -301.8 0.0143465 -301.9 0.0146464 -302 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0.640521 -1979.2 0.276533 -1979.3 0.383048 -1979.4 0.0725334 -1979.5 0.656226 -1979.6 0.798652 -1979.7 0.887921 -1979.8 0.455812 -1979.9 0.346208 -1980 0.459275 -1980.1 0.558363 -1980.2 0.910115 -1980.3 0.942505 -1980.4 0.914627 -1980.5 0.95432 -1980.6 0.93232 -1980.7 0.961145 -1980.8 0.963024 -1980.9 0.959745 -1981 0.923275 -1981.1 0.963238 -1981.2 0.909931 -1981.3 0.936772 -1981.4 0.972431 -1981.5 0.943509 -1981.6 0.962858 -1981.7 0.976022 -1981.8 0.973236 -1981.9 0.865589 -1982 0.945424 -1982.1 0.876859 -1982.2 0.937476 -1982.3 0.796685 -1982.4 0.868225 -1982.5 0.573059 -1982.6 0.809069 -1982.7 0.959217 -1982.8 0.968565 -1982.9 0.965721 -1983 0.97304 -1983.1 0.978613 -1983.2 0.982725 -1983.3 0.979458 -1983.4 0.94848 -1983.5 0.979784 -1983.6 0.978272 -1983.7 0.965978 -1983.8 0.957107 -1983.9 0.914017 -1984 0.969845 -1984.1 0.977226 -1984.2 0.977027 -1984.3 0.970775 -1984.4 0.967531 -1984.5 0.951088 -1984.6 0.819598 -1984.7 0.394108 -1984.8 0.886498 -1984.9 0.953119 -1985 0.973426 -1985.1 0.979814 -1985.2 0.967614 -1985.3 0.974074 -1985.4 0.981023 -1985.5 0.959925 -1985.6 0.971838 -1985.7 0.977391 -1985.8 0.955061 -1985.9 0.955789 -1986 0.960285 -1986.1 0.905398 -1986.2 0.863501 -1986.3 0.73379 -1986.4 0.145278 -1986.5 0.469034 -1986.6 0.182426 -1986.7 0.777217 -1986.8 0.90696 -1986.9 0.947192 -1987 0.939746 -1987.1 0.932411 -1987.2 0.946384 -1987.3 0.97054 -1987.4 0.853202 -1987.5 0.955785 -1987.6 0.968449 -1987.7 0.919426 -1987.8 0.96995 -1987.9 0.951668 -1988 0.967182 -1988.1 0.974426 -1988.2 0.964544 -1988.3 0.978131 -1988.4 0.964988 -1988.5 0.906649 -1988.6 0.965928 -1988.7 0.963164 -1988.8 0.981481 -1988.9 0.971951 -1989 0.962111 -1989.1 0.950291 -1989.2 0.856821 -1989.3 0.561972 -1989.4 0.90564 -1989.5 0.850007 -1989.6 0.296463 -1989.7 0.447882 -1989.8 0.74509 -1989.9 0.898865 -1990 0.902624 -1990.1 0.835298 -1990.2 0.384238 -1990.3 0.868381 -1990.4 0.954363 -1990.5 0.967643 -1990.6 0.973453 -1990.7 0.96853 -1990.8 0.983873 -1990.9 0.984141 -1991 0.967026 -1991.1 0.972979 -1991.2 0.982158 -1991.3 0.952498 -1991.4 0.875369 -1991.5 0.970989 -1991.6 0.974594 -1991.7 0.976433 -1991.8 0.953159 -1991.9 0.945323 -1992 0.951834 -1992.1 0.970532 -1992.2 0.963272 -1992.3 0.97944 -1992.4 0.983596 -1992.5 0.963106 -1992.6 0.969187 -1992.7 0.972718 -1992.8 0.948098 -1992.9 0.949743 -1993 0.956457 -1993.1 0.971289 -1993.2 0.978259 -1993.3 0.946388 -1993.4 0.94078 -1993.5 0.926271 -1993.6 0.458787 -1993.7 0.580269 -1993.8 0.845279 -1993.9 0.960268 -1994 0.952568 -1994.1 0.909697 -1994.2 0.75215 -1994.3 0.960713 -1994.4 0.927665 -1994.5 0.970124 -1994.6 0.889314 -1994.7 0.892882 -1994.8 0.963405 -1994.9 0.876903 -1995 0.879628 -1995.1 0.428431 -1995.2 0.703112 -1995.3 0.938829 -1995.4 0.86425 -1995.5 0.810107 -1995.6 0.865226 -1995.7 0.752976 -1995.8 0.859116 -1995.9 0.934369 -1996 0.613265 -1996.1 0.783658 -1996.2 0.47614 -1996.3 0.54376 -1996.4 0.711266 -1996.5 0.763525 -1996.6 0.768158 -1996.7 0.491643 -1996.8 0.90701 -1996.9 0.853669 -1997 0.298941 -1997.1 0.810118 -1997.2 0.827857 -1997.3 0.487051 -1997.4 0.470999 -1997.5 0.629965 -1997.6 0.297728 -1997.7 0.0877002 -1997.8 0.612109 -1997.9 0.810688 -1998 0.516076 -1998.1 0.158291 -1998.2 0.720971 -1998.3 0.763931 -1998.4 0.377975 -1998.5 0.199722 -1998.6 0.598737 -1998.7 0.737947 -1998.8 0.280141 -1998.9 0.191826 -1999 0.673947 -1999.1 0.557944 -1999.2 0.259748 -1999.3 0.115745 -1999.4 0.590227 -1999.5 0.682843 -1999.6 0.317758 -1999.7 0.0298489 -1999.8 0.488221 -1999.9 0.656683 -2000 0.428576 -2000.1 0.0149049 -2000.2 0.200153 -2000.3 0.577065 -2000.4 0.528517 -2000.5 0.106341 -2000.6 0.0434723 -2000.7 0.306331 -2000.8 0.462451 -2000.9 0.308658 -2001 0.00433162 -2001.1 0.179435 -2001.2 0.516574 -2001.3 0.489162 -2001.4 0.115603 -2001.5 0.00480734 -2001.6 0.274853 -2001.7 0.418963 -2001.8 0.287925 -2001.9 0.0522004 -2002 0.0163982 -2002.1 0.29401 -2002.2 0.367953 -2002.3 0.088371 -2002.4 0.0248674 -2002.5 0.0210577 -2002.6 0.280009 -2002.7 0.45184 -2002.8 0.31011 -2002.9 0.0309803 -2003 0.0124718 -2003.1 0.257781 -2003.2 0.4164 -2003.3 0.358723 -2003.4 0.0554152 -2003.5 0.00212081 -2003.6 0.181026 -2003.7 0.434636 -2003.8 0.401683 -2003.9 0.104667 -2004 1.01741e-05 -2004.1 0.105056 -2004.2 0.362438 -2004.3 0.454953 -2004.4 0.174442 -2004.5 0.0100307 -2004.6 0.0236434 -2004.7 0.260301 -2004.8 0.354881 -2004.9 0.271957 -2005 0.106881 -2005.1 8.41368e-05 -2005.2 0.121179 -2005.3 0.155029 -2005.4 0.451732 -2005.5 0.270327 -2005.6 0.0346119 -2005.7 0.005307 -2005.8 0.138566 -2005.9 0.515226 -2006 0.563317 -2006.1 0.337201 -2006.2 0.0152067 -2006.3 0.0334819 -2006.4 0.400117 -2006.5 0.597666 -2006.6 0.59419 -2006.7 0.223453 -2006.8 0.0164417 -2006.9 0.115695 -2007 0.552607 -2007.1 0.678582 -2007.2 0.407895 -2007.3 0.368462 -2007.4 0.0338813 -2007.5 0.212075 -2007.6 0.538208 -2007.7 0.446184 -2007.8 0.683647 -2007.9 0.46569 -2008 0.288497 -2008.1 0.339261 -2008.2 0.424513 -2008.3 0.613256 -2008.4 0.467442 -2008.5 0.847577 -2008.6 0.865387 -2008.7 0.54507 -2008.8 0.631677 -2008.9 0.384329 -2009 0.0277829 -2009.1 0.527445 -2009.2 0.669721 -2009.3 0.443491 -2009.4 0.695496 -2009.5 0.289279 -2009.6 0.0468216 -2009.7 0.091936 -2009.8 0.296171 -2009.9 0.633053 -2010 0.649217 -2010.1 0.341562 -2010.2 0.206067 -2010.3 0.00108446 -2010.4 0.15283 -2010.5 0.483855 -2010.6 0.55416 -2010.7 0.453223 -2010.8 0.43342 -2010.9 0.064614 -2011 0.0140125 -2011.1 0.334007 -2011.2 0.41895 -2011.3 0.512131 -2011.4 0.581451 -2011.5 0.319934 -2011.6 0.00841304 -2011.7 0.0325701 -2011.8 0.289572 -2011.9 0.507344 -2012 0.56033 -2012.1 0.522346 -2012.2 0.234737 -2012.3 0.0125802 -2012.4 0.0290641 -2012.5 0.349623 -2012.6 0.608556 -2012.7 0.621784 -2012.8 0.44963 -2012.9 0.21241 -2013 0.0148769 -2013.1 0.0259617 -2013.2 0.363695 -2013.3 0.591483 -2013.4 0.481689 -2013.5 0.473488 -2013.6 0.343623 -2013.7 0.0514594 -2013.8 0.00714348 -2013.9 0.289633 -2014 0.523187 -2014.1 0.652786 -2014.2 0.650061 -2014.3 0.439015 -2014.4 0.100094 -2014.5 8.41724e-05 -2014.6 0.157881 -2014.7 0.419485 -2014.8 0.634524 -2014.9 0.65903 -2015 0.446212 -2015.1 0.340622 -2015.2 0.0202147 -2015.3 0.019789 -2015.4 0.392406 -2015.5 0.640571 -2015.6 0.670845 -2015.7 0.421123 -2015.8 0.535787 -2015.9 0.19583 -2016 0.000306121 -2016.1 0.197438 -2016.2 0.436105 -2016.3 0.525209 -2016.4 0.267962 -2016.5 0.602788 -2016.6 0.494479 -2016.7 0.0530198 -2016.8 0.0167076 -2016.9 0.386169 -2017 0.477862 -2017.1 0.607813 -2017.2 0.733964 -2017.3 0.643094 -2017.4 0.257054 -2017.5 0.0620653 -2017.6 0.0570797 -2017.7 0.342001 -2017.8 0.539798 -2017.9 0.777405 -2018 0.706885 -2018.1 0.410114 -2018.2 0.514923 -2018.3 0.067308 -2018.4 0.0808585 -2018.5 0.399754 -2018.6 0.728552 -2018.7 0.693256 -2018.8 0.446654 -2018.9 0.728401 -2019 0.604898 -2019.1 0.119375 -2019.2 0.0854386 -2019.3 0.605154 -2019.4 0.676018 -2019.5 0.427839 -2019.6 0.803814 -2019.7 0.794967 -2019.8 0.67004 -2019.9 0.220352 -2020 0.0703897 -2020.1 0.604538 -2020.2 0.575483 -2020.3 0.666655 -2020.4 0.607161 -2020.5 0.797455 -2020.6 0.735303 -2020.7 0.289914 -2020.8 0.0255987 -2020.9 0.480843 -2021 0.504079 -2021.1 0.845547 -2021.2 0.85149 -2021.3 0.878426 -2021.4 0.710875 -2021.5 0.565472 -2021.6 0.218999 -2021.7 0.150311 -2021.8 0.673591 -2021.9 0.846697 -2022 0.915904 -2022.1 0.910503 -2022.2 0.629267 -2022.3 0.785586 -2022.4 0.653831 -2022.5 0.0691957 -2022.6 0.602068 -2022.7 0.635397 -2022.8 0.808446 -2022.9 0.91095 -2023 0.634003 -2023.1 0.865687 -2023.2 0.838276 -2023.3 0.353515 -2023.4 0.20753 -2023.5 0.817826 -2023.6 0.873724 -2023.7 0.914126 -2023.8 0.69825 -2023.9 0.899605 -2024 0.853046 -2024.1 0.6551 -2024.2 0.462275 -2024.3 0.40792 -2024.4 0.865489 -2024.5 0.852234 -2024.6 0.758687 -2024.7 0.861658 -2024.8 0.885337 -2024.9 0.760933 -2025 0.890176 -2025.1 0.50778 -2025.2 0.516344 -2025.3 0.900968 -2025.4 0.87428 -2025.5 0.79385 -2025.6 0.914874 -2025.7 0.798931 -2025.8 0.939781 -2025.9 0.916855 -2026 0.63436 -2026.1 0.522789 -2026.2 0.912458 -2026.3 0.79225 -2026.4 0.942918 -2026.5 0.823357 -2026.6 0.940321 -2026.7 0.93872 -2026.8 0.921048 -2026.9 0.82812 -2027 0.46958 -2027.1 0.855556 -2027.2 0.808285 -2027.3 0.852212 -2027.4 0.925956 -2027.5 0.887496 -2027.6 0.65119 -2027.7 0.894769 -2027.8 0.918169 -2027.9 0.518378 -2028 0.808783 -2028.1 0.884016 -2028.2 0.632859 -2028.3 0.889822 -2028.4 0.878742 -2028.5 0.957707 -2028.6 0.954515 -2028.7 0.949058 -2028.8 0.805452 -2028.9 0.650288 -2029 0.829766 -2029.1 0.938311 -2029.2 0.978073 -2029.3 0.947544 -2029.4 0.973348 -2029.5 0.953134 -2029.6 0.921735 -2029.7 0.874067 -2029.8 0.663976 -2029.9 0.877823 -2030 0.871127 -2030.1 0.950418 -2030.2 0.941518 -2030.3 0.924102 -2030.4 0.883568 -2030.5 0.933917 -2030.6 0.928932 -2030.7 0.887575 -2030.8 0.738614 -2030.9 0.964575 -2031 0.961664 -2031.1 0.933445 -2031.2 0.96207 -2031.3 0.897715 -2031.4 0.971259 -2031.5 0.906287 -2031.6 0.920988 -2031.7 0.893149 -2031.8 0.84185 -2031.9 0.959621 -2032 0.911391 -2032.1 0.946224 -2032.2 0.916009 -2032.3 0.958149 -2032.4 0.964387 -2032.5 0.871689 -2032.6 0.817434 -2032.7 0.740409 -2032.8 0.815822 -2032.9 0.911731 -2033 0.905698 -2033.1 0.965597 -2033.2 0.889486 -2033.3 0.967917 -2033.4 0.949711 -2033.5 0.881183 -2033.6 0.973833 -2033.7 0.881754 -2033.8 0.916613 -2033.9 0.956355 -2034 0.889623 -2034.1 0.976181 -2034.2 0.936597 -2034.3 0.904555 -2034.4 0.966177 -2034.5 0.892365 -2034.6 0.952813 -2034.7 0.940578 -2034.8 0.884044 -2034.9 0.978195 -2035 0.955859 -2035.1 0.874901 -2035.2 0.974518 -2035.3 0.917795 -2035.4 0.938943 -2035.5 0.913243 -2035.6 0.917042 -2035.7 0.974376 -2035.8 0.956207 -2035.9 0.858871 -2036 0.951049 -2036.1 0.977222 -2036.2 0.9188 -2036.3 0.961807 -2036.4 0.864063 -2036.5 0.937942 -2036.6 0.981529 -2036.7 0.958941 -2036.8 0.909354 -2036.9 0.881402 -2037 0.955122 -2037.1 0.960432 -2037.2 0.975983 -2037.3 0.825305 -2037.4 0.952813 -2037.5 0.969792 -2037.6 0.961152 -2037.7 0.967055 -2037.8 0.735333 -2037.9 0.921528 -2038 0.969301 -2038.1 0.976138 -2038.2 0.976437 -2038.3 0.782707 -2038.4 0.945774 -2038.5 0.976736 -2038.6 0.979919 -2038.7 0.974906 -2038.8 0.768157 -2038.9 0.935993 -2039 0.977336 -2039.1 0.977054 -2039.2 0.950252 -2039.3 0.800104 -2039.4 0.872798 -2039.5 0.744479 -2039.6 0.937265 -2039.7 0.820466 -2039.8 0.882718 -2039.9 0.705318 -2040 0.890466 -2040.1 0.92811 -2040.2 0.969364 -2040.3 0.963814 -2040.4 0.704062 -2040.5 0.930487 -2040.6 0.978044 -2040.7 0.956983 -2040.8 0.921276 -2040.9 0.941807 -2041 0.728139 -2041.1 0.910588 -2041.2 0.97755 -2041.3 0.978018 -2041.4 0.912598 -2041.5 0.799926 -2041.6 0.868401 -2041.7 0.958428 -2041.8 0.923333 -2041.9 0.978812 -2042 0.969929 -2042.1 0.694898 -2042.2 0.937686 -2042.3 0.973471 -2042.4 0.891056 -2042.5 0.934977 -2042.6 0.95327 -2042.7 0.753224 -2042.8 0.878176 -2042.9 0.964458 -2043 0.977152 -2043.1 0.848966 -2043.2 0.943709 -2043.3 0.768883 -2043.4 0.943677 -2043.5 0.882325 -2043.6 0.883302 -2043.7 0.902081 -2043.8 0.887758 -2043.9 0.829854 -2044 0.936556 -2044.1 0.918932 -2044.2 0.903011 -2044.3 0.917844 -2044.4 0.968906 -2044.5 0.839831 -2044.6 0.865015 -2044.7 0.973193 -2044.8 0.969179 -2044.9 0.870982 -2045 0.882342 -2045.1 0.977198 -2045.2 0.929806 -2045.3 0.912794 -2045.4 0.806851 -2045.5 0.96676 -2045.6 0.929119 -2045.7 0.962596 -2045.8 0.92782 -2045.9 0.767999 -2046 0.914867 -2046.1 0.966829 -2046.2 0.900835 -2046.3 0.727303 -2046.4 0.832979 -2046.5 0.966048 -2046.6 0.974426 -2046.7 0.931704 -2046.8 0.5371 -2046.9 0.855612 -2047 0.958339 -2047.1 0.92281 -2047.2 0.925271 -2047.3 0.521159 -2047.4 0.90802 -2047.5 0.772241 -2047.6 0.918242 -2047.7 0.906014 -2047.8 0.441612 -2047.9 0.864462 -2048 0.945735 -2048.1 0.942111 -2048.2 0.708666 -2048.3 0.344492 -2048.4 0.871176 -2048.5 0.940505 -2048.6 0.94527 -2048.7 0.863886 -2048.8 0.325739 -2048.9 0.599665 -2049 0.893094 -2049.1 0.909721 -2049.2 0.900708 -2049.3 0.416157 -2049.4 0.584688 -2049.5 0.886688 -2049.6 0.754679 -2049.7 0.803575 -2049.8 0.611959 -2049.9 0.286649 -2050 0.797239 -2050.1 0.91492 -2050.2 0.906492 -2050.3 0.736694 -2050.4 0.103034 -2050.5 0.707461 -2050.6 0.892625 -2050.7 0.900023 -2050.8 0.862658 -2050.9 0.41625 -2051 0.29931 -2051.1 0.605632 -2051.2 0.882304 -2051.3 0.888674 -2051.4 0.735267 -2051.5 0.10587 -2051.6 0.61398 -2051.7 0.865885 -2051.8 0.820553 -2051.9 0.677636 -2052 0.473591 -2052.1 0.147727 -2052.2 0.747868 -2052.3 0.865068 -2052.4 0.879815 -2052.5 0.779441 -2052.6 0.223587 -2052.7 0.293935 -2052.8 0.763062 -2052.9 0.866994 -2053 0.827968 -2053.1 0.697529 -2053.2 0.105502 -2053.3 0.419312 -2053.4 0.637177 -2053.5 0.856694 -2053.6 0.827921 -2053.7 0.592495 -2053.8 0.0506544 -2053.9 0.50004 -2054 0.813974 -2054.1 0.847821 -2054.2 0.670516 -2054.3 0.575005 -2054.4 0.0364452 -2054.5 0.525109 -2054.6 0.818715 -2054.7 0.865966 -2054.8 0.82997 -2054.9 0.570495 -2055 0.0342713 -2055.1 0.510198 -2055.2 0.819678 -2055.3 0.860682 -2055.4 0.841807 -2055.5 0.633565 -2055.6 0.0578994 -2055.7 0.448205 -2055.8 0.691645 -2055.9 0.854615 -2056 0.825729 -2056.1 0.5952 -2056.2 0.127917 -2056.3 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+60,21 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created TAP+ (v1.0.1) - Connection:\n", + "\tHost: gea.esac.esa.int\n", + "\tUse HTTPS: False\n", + "\tPort: 80\n", + "\tSSL Port: 443\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -100,10 +110,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -111,6 +119,38 @@ "text": [ "Query finished.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:7:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:8:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:10:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W27: None:11:0: W27: COOSYS deprecated in VOTable 1.2 [astropy.io.votable.tree]\n", + "WARNING: W06: None:48:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:51:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:54:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:57:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:60:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:123:0: W50: Invalid unit string 'mas**-2' [astropy.io.votable.tree]\n", + "WARNING: W50: None:126:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:129:0: W06: Invalid UCD 'em.wavenumber;stat.error': Primary word 'stat.error' is not valid as a secondary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:129:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:141:0: W06: Invalid UCD 'pos.errorEllipse;stat.max': Secondary word 'pos.errorEllipse' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:156:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:159:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:162:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:171:0: W50: Invalid unit string ''electron'.s**-1' (suppressing further warnings of this type...) [astropy.io.votable.tree]\n", + "WARNING: W06: None:177:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W06: None:192:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W06: None:204:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" + ] } ], "source": [ @@ -143,10 +183,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "T_eff = round(T_eff, -2) # round to nearest 100 K\n", @@ -172,10 +210,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { @@ -224,10 +260,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", @@ -252,10 +286,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -299,19 +331,69 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We use the Cerro Paranal model transmittance curves for an airmass of 1.5:" + "The following cells show how to simulate an atmospheric transmission profile at a given site and time of year. We use `pwv_kpno`, a package that downloads data collected by SuomiNet on the precipitable water vapor in the atmosphere on a given night at the given location, and uses them to calculate the transmission as a function of wavelength. The list of sites from which data is collected can be seen here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next cell demonstrates how to configure a `kpno_pwv` observation site other than the default (aka Kitt Peak). Once this cell has been ran, it does not need to be ran again." ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "[2018]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pwv_kpno.package_settings import ConfigBuilder, settings\n", + "from pwv_kpno import pwv_atm\n", + "from datetime import datetime\n", + "import pytz\n", + "\n", + "# configure the new observation site\n", + "new_config = ConfigBuilder(\n", + " site_name = \"apache_point\",\n", + " primary_rec=\"P027\", # the suominet receiver nearest APO\n", + " sup_rec = []\n", + " )\n", + "# new_config.save_to_ecsv('./apache_point.ecsv')\n", + "# settings.import_site_config('./apache_point.ecsv')\n", + "\n", + "# change site from KPNO to APO\n", + "settings.set_site('apache_point')\n", + "\n", + "# download data from SuomiNet from year observation was taken\n", + "pwv_atm.update_models(years=[2018])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we combine with `synphot` so we can convolve the atmospheric effects with other parts of the observation:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" ] @@ -323,8 +405,13 @@ } ], "source": [ - "atmosphere = SpectralElement.from_file('CerroParanal-atmosx1.5.txt', wave_unit=\"nm\")\n", - "atmosphere.plot(left=3000, right=14000)" + "obsv_date = datetime(year=2018, month=5, day=15) # set the date of observation, let's say mid-May\n", + "transmission = pwv_atm.trans_for_date(date=obsv_date, airmass=1.5) # get the transmission function\n", + "\n", + "atmosphere = SpectralElement(Empirical1D,\n", + " points=transmission['wavelength'],\n", + " lookup_table=transmission['transmission'])\n", + "atmosphere.plot()" ] }, { @@ -344,10 +431,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -386,14 +471,12 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -421,10 +504,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "band = 'r' # only want to know about the r-band\n", @@ -452,10 +533,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "waverange = (3000, 11300) # according to the ARCTIC webpage, the z band is cut off around 11k AA\n", @@ -472,17 +551,15 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HAT-P-11 counts in the r-band, 10 s exposure:\n", - "38324129 cts\n" + "44167476 cts\n" ] } ], @@ -512,10 +589,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": true - }, + "execution_count": 32, + "metadata": {}, "outputs": [], "source": [ "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", @@ -540,9 +615,8 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 33, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -580,17 +654,15 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "311256 cts\n" + "308255 cts\n" ] } ], @@ -625,10 +697,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -645,10 +715,8 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -672,24 +740,22 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 47, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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16nHmzBmWLFnChAkTNFnkEq1hKKXyhG+++Ybw8HB+/fVXOnfuzPvvv0/x4sWdDitf8Ykahg4NopTv+vPPP3n11Vd57LHHKFKkCDExMYwZM0aThQNsJQwReVZEGqd6/T8ROSgiS0Xkds+FZ48ODaKUb5o9ezb+/v5MmjSJXr16sX37durVq+d0WPmW3RpGv5RfRKQ68F9gNFAIGO7+sJRS+dnvv/9Oq1at+M9//kOZMmXYtGkT77//vs4f7zC7CeMuYLfr96eBr40xHwJvAo08EZhSKv8xxjBx4kT8/f1ZsGABgwcPZuPGjVSrVs3p0BT2G73PASk3DBsBn7t+T0xVrpRS2fbLL7/Qvn17li5dSr169YiKiuK+++5zOiyVit0axhpguIj0AYKBRa7ye4EDnghMKZU/JCcnM3bsWCpXrszatWsZO3Ys3377rSYLL2Q3YXQBLgCtgA7GmMOu8qbAUk8EppTyfT/88AMPPfQQXbt2pV69esTFxdG5c2cKFPCJBzh9jq1bUsaYg0CLdMq7uT0ipZTPS0pKYujQofTv359ixYoxceJEXnrpJUTE6dDUNdh9rHafiJRKp/xGEdnn/rCUUr5q27Zt1KpVi969e/PEE0+wa9cu2rZtq8kiD7Bb7ysPFEyn/DqgnNuiUUr5rHPnztGrVy9q1qzJ77//zuzZs5kxYwa33Xab06Epm655S0pEWqZ62UxEUnelLoj1xNTPnghMKeU7vvvuO0JCQvjxxx9p164dw4YN46abbnI6LJVFmbVhzHT9NEB0mnVJwH6gu5tjUkr5iJMnT9KrVy8iIiIoX748y5Yt45FHHnE6LJVN10wYxpgCACLyM1DTGHMsV6JSSuV5S5YsoX379hw4cIDXX3+dgQMHcsMNNzgdlsoBu09J6TyHSilbjh8/zptvvskXX3yBn58fa9eupU6dOk6HpdzA9vDmIlIbq83iVtI0lhtjXnNzXEqpPMYYw6xZs+jcuTMnTpygT58+9O7dm+uuu87p0JSb2EoYIvIW8CGwBziM1aaRwqS7k1Iq3/jtt9/o3Lkzc+bMoUaNGnzzzTdUrVrV6bCUm9mtYbwOvGaMGevJYFITkfrAC1gx+htj6ubWuZVS9hhjGD9+PG+++Sbnz5/nww8/5I033uD//k/nZvNFdvthlOCf8aOyTUQ+F5EjIhKXpryJiOwWkT0i0hPAGLPGGNMBWABMzOm5lVLu9fPPP9O4cWNCQkKoWrUqO3bsoEePHposfJjdhDEVaOKG801IexwRKQhEYI1L5Q+0FhH/VJu0cZ1fKeUFLl26xEcffURAQAAbNmzgk08+YdWqVdx7771Oh6Y8zO5/BQ4A/UXkQSAWqw/GZcaYEXYOYoyJEZHyaYprAXuMMfsARGQa8CSQICJ3AonGmL9txqmU8qCEhARCQ0NZv349jz/+OJGRkdxxxx1Oh6Vyid2EEQqcAuq6ltQMYCthZKAsVw6RfhCo7fo9BBh/rZ1FJBwIB7jzzjtzEIZSKiMXLlzgww8/ZMCAARQvXpzJkyfTpk0bHf8pn/GGfhjp/YszrvP2zWxnY8w4YBxAcHCwPrGllJtt3ryZkJAQYmNjef755/noo4+49dZbnQ5LOcAbBp0/CKSu05bDenRXKeWgs2fP8vbbb1O7dm2OHTvG3LlzmTp1qiaLfMxuP4zR11qfw457m4B7RKQCcAh4Hquh2zYRaQG0qFSpUg7CUEql+PbbbwkNDWXPnj2EhYXx4YcfcuONNzodlnKY3RpGlTRLdawv9bZAgN2TichUYD1wn4gcFJEQY8xFrBn9lgK7gOnGmHj7bwGMMfONMeElS5bMym5KqTT+/vtvOnbsyMMPP0xycjIrVqxg3LhxmiwUYL8No2HaMhEpgjWC7Rq7JzPGtM6gfBFu6OehlMq+hQsX0qFDBw4fPsybb77JgAEDKFq0qNNhKS+S7TYMY8w5YBDQ233hKKVy27Fjx3jxxRdp3rw5JUuWZN26dQwfPlyThbpKThu9SwOOj1csIi1EZFxiYmLmGyulAGtYj2nTpuHn58f06dPp27cvW7dupXbt2pnvrPIlu43eb6YtAm7HGuvJ8VtJxpj5wPzg4OAwp2NRKi84dOgQnTp1Yt68edSsWZPo6GiqVKnidFjKy9ntuNc1zetk4ChWp7rBbo1IKeUxxhiioqJ46623SEpKYvjw4bz++usULFjQ6dBUHuANHfeUUrlg7969hIWFsWrVKho2bMhnn31GxYoVnQ5L5SFZbsMQkRtEpJgngskubcNQKmOXLl1ixIgRVKlShS1btjBu3DhWrFihyUJlme2EISKdReRXIBH4W0R+EZFOngvNPu2HoVT64uLiqFu3Lt27d+eRRx4hISGBsLAwHQNKZYuthCEi/wWGYPW7aOxaxgNDUuavUEp5jwsXLtC/f3+qV6/Ovn37mDp1KnPnzqVs2bJOh6byMLuN3h2AcGNM6nkpVojIT8D7WMlEKeUFNm7cSEhICHFxcbzwwguMGjWKW265xemwlA+we0vqVqwxn9LaCNzmvnCyR9swlIIzZ87QvXt36tSpw19//cWCBQuYPHmyJgvlNnYTxo+kPyBgG2C3+8LJHm3DUPndqlWrqFKlCiNGjCA8PJz4+HiaNWvmdFjKx9i9JdUPmC4iDwFrsearqAc0AJ7xTGhKqcwkJibSo0cPPvvsMypVqsTq1atp0KCB02EpH2WrhmGMmY01C97vQHPgCdfvtYwxX3suPKVURubPn4+/vz/R0dG89dZb7NixQ5OF8ii7NQyMMVuAFz0Yi1LKhqNHj/Laa68xbdo0qlSpwty5cwkODnY6LJUP2H2s9hkReTKd8idFpJX7w8oabfRW+YExhi+//BI/Pz9mz57NgAED2Lx5syYLlWvsNnr3A86lU37atc5R2uitfN2BAwdo0aIFL7zwAvfccw/btm3j3XffpXDhwk6HpvIRuwnjbtJ/GmqPa51SygOSk5OJjIykcuXKrFq1ilGjRvHdd9/h7+/vdGgqH7LbhvEncA+wP035vcBJdwaklLL89NNPhIWF8e2339KoUSPGjRvH3Xfr/8+Uc+zWMOYCI0Xk3pQCEbkPGAHoU1JKudHFixcZOnQogYGBbN++nejoaJYtW6bJQjnObg3jbWAJkCAiv7nKbsfq6d3DE4EplR/FxsYSEhLC5s2beeqpp4iIiOBf//qX02EpBdifD+Mk8KCIPAoEYc24txVYYYwxHoxPqXzh/PnzDBo0iMGDB3PzzTczffp0WrVqpaPKKq9iux8GgDFmGbDMQ7Fkm4i0AFpUqlTJ6VCUyrL169cTEhLCrl27aNu2LSNGjKBUqVJOh6XUVbI8gZI30sdqVV50+vRpunXrxoMPPsipU6dYtGgREydO1GShvFaWahhKKfdYvnw5YWFh7N+/n86dOzN48GCKFy/udFhKXZNP1DCUyiv++usvQkJCePTRRylUqBAxMTGMHTtWk4XKEzRhKJVLvv76a/z9/Zk4cSI9e/Zkx44d1K9f3+mwlLLN7lhS+0TkqhurInKjiOxzf1hK+Y4//viDZ599lqeffprbbruNjRs3MnjwYK6//nqnQ1MqS+zWMMoDBdMpvw7QSYKVSocxhi+++AI/Pz/mzp3LoEGD2LhxI9WrV3c6NKWy5ZqN3iLSMtXLZiKSejjYgkAjrh4uRKl879dff6V9+/YsWbKEunXrEh0dzf333+90WErlSGZPSc10/TRAdJp1SVjJorubY8oy7YehvEVycjKffPIJPXv2xBjD6NGj6dy5MwUKaHOhyvuu+a/YGFPAGFMA+BW4NeW1a7nOGHOfMWZB7oR6zTi1H4Zy3O7du2nQoAFdunShbt26xMXF0bVrV00WymfYnaK1gjHmmKeDUSovunjxIkOGDKFq1arEx8czYcIElixZQvny5Z0OTSm3sv1fHxFpJiIxInJMRI6KyLci8rgng1PK223fvp3atWvTq1cvmjdvTkJCAi+//LKOAaV8kt3HakOBOcBe4B2gJ/AzMEdE2nkuPKW807lz5+jduzfBwcEcOnSImTNnMnPmTMqUKeN0aEp5jN2hQd4B3jTGjE1VFi0iW7CSx+duj0wpL7V27VpCQkLYvXs3r7zyCsOHD+fmm292OiylPM7uLak7sebDSGsxcJf7wlHKe506dYrXXnuN+vXrc+7cOZYuXcr48eM1Wah8w27C+BV4NJ3yxsAv7gtHKe/0zTffEBAQwNixY+natStxcXE0btzY6bCUylV2b0kNA8aISHVgHVa/jHrAS0BXD8WmlONOnDhB9+7dmTBhAvfffz9r1qzhwQcfdDospRxhd8a9T0XkCFYnvZTe37uAZ40xcz0VnFJOmjVrFp07d+bYsWP07t2bd999lyJFijgdllKOsT0fhjFmDtaTUkr5tN9++40uXbowe/ZsqlWrxpIlSwgKCnI6LKUcl+UuqK4Ram9OvXgisCzG1EJExiUmJma+sVIZMMYwYcIE/P39WbhwIUOGDGHjxo2aLJRysdsP4y4RWSwi54DjwFHXcsz101E6NIjKqf379/PYY4/x6quvEhAQwI4dO3jnnXf4v//TSSmVSmH3r2E8cCPQDjiM1eitVJ6XnJxMREQEvXr1QkSIiIigQ4cOOv6TUumwmzBqAQ8YY+I8GYxSuWnXrl2Ehoaybt06mjRpwqeffsqdd97pdFhKeS27/436GWuyJKXyvKSkJAYNGkRQUBA//PADX3zxBYsWLdJkoVQm7CaM14HBIqITTqg8bevWrdSsWZN3332Xp556ioSEBF566SUdLFApGzK8JSUiJ7myraIIsFtEzgMXU29rjCnhmfCUco+zZ8/Sv39/hg0bRunSpZkzZw5PPfWU02Epladcqw2jS65FoZQHrVmzhtDQUH788UdCQkIYOnQoN910k9NhKZXnZJgwjDETczMQpdzt5MmT9OzZk48//pgKFSqwfPlyGjVq5HRYSuVZth8yF5EiQBvA31WUAEw1xpz1RGBK5cTixYtp3749Bw8epFu3bgwcOJBixYo5HZZSeZrdjnvVsZ6UGo71iG0trAEJ97nWKeUVjh8/Ttu2bXn88ccpXrw469atY+TIkZoslHIDu09JjQPWAOWMMQ8ZYx4C7gBiXOuUcpQxhunTp+Pn58fUqVPp06cPW7du5YEHHnA6NKV8ht1bUpWBtsaY0ykFxpjTIvIesNkjkSll0+HDh+ncuTNff/01NWrUYPny5QQGBjodllI+x24N4wfgX+mU3w786L5wlLLPGEN0dDT+/v4sWbKEoUOH8v3332uyUMpD7NYw3gVGu2oU37vKHnCV90w9Yq0x5oR7Q1Tqavv27SM8PJwVK1bQoEEDoqKiqFRJ+5Uq5Ul2E8Z8188v+aczX0rX2LmpXhugoHtCU+pqly5dYsyYMfTu3ZuCBQsSGRlJWFiYDhaoVC6wmzAaejSKdIhIAWAAUALYrP1CVHx8PCEhIWzYsIFmzZoRGRlJuXLlnA5LqXzD7hSt37rjZCLyOdAcOGKMCUhV3gT4CKt2EmWMGQI8CZQFTgAH3XF+lTdduHCBDz74gAEDBlCiRAmmTJlC69atdfwnpXJZVjruFQWCgFtJ01hujJlt8zATgLHAF6mOWxCIAB7FSgybRGQecB+w3jWf+Exghd1Yle/YtGkTISEh7Ny5k+eff57Ro0dTunRpp8NSKl+ylTBE5BFgKlAqndW22y2MMTEiUj5NcS1gjzFmn+tc07BqFweAC65tLtk5vvIdZ86coV+/fgwfPpwyZcowd+5cnnjiCafDUipfs9tS+BGwEKvjXoE0S04buctiJYcUB11ls4HHRGQMVgfBdIlIuIhsFpHNR486PluscoPVq1dTtWpVhg4dSmhoKAkJCZoslPICdm9JlQeeMMYc9kAM6d2INsaYM0BIZjsbY8bh6m0eHBysU8fmYYmJibzzzjt8+umnVKxYkZUrV9KwYa4/b6GUyoDdGsZarDYFTziINcxIinJY84arfGThwoVUrlyZzz77jO7duxMbG6vJQikvY7eGEQkME5F/ATuBpNQrjTFbcxDDJuAeEakAHAKexxoV1zYRaQG00I5bec/Ro0fp1q0bX375JQEBAcyePZtatWo5HZZSKh12PraHAAAT30lEQVRiTOZ3cUQk+Rqrjd12DBGZCjwM3AL8AfQ1xkSLyOPAKKzG88+NMYPsHC+t4OBgs3mzDm2VFxhj+Oqrr+jatSuJiYn07t2bXr16UbhwYadDUyrfEZEtxpjgzLazW8OokMN4ADDGtM6gfBGwyB3nUN7v0KFDdOzYkfnz51OrVi2io6MJCAjIfEellKPsJowzxhh9BEnlSHJyMlFRUfTo0YOkpCRGjBjBa6+9RsGCOpqMUnmB3UbvQyIyS0Saihd2rxWRFiIyLjEx0elQVAb27NlDo0aNaN++PTVq1GDnzp288cYbmiyUykPsJoxmwHlgFnBARAaISEXPhZU1xpj5xpjwkiVLOh2KSuPSpUsMHz6cwMBAtm7dymeffcaKFSuoWNFr/vkopWyylTCMMcuMMW2w5sQYDDQFfhSRlSLygmu+b6WuEBcXR506dXjrrbd45JFHSEhIIDQ0VMeAUiqPytKY0MaYv4wxEa7W9NeAusAk4LCIDBGRGzwRZGb0lpR3uXDhAv369aN69ers37+fadOmMXfuXMqWLet0aEqpHMhSwhCR20Wkp4jsBj4ApgENgI5AE+Br94eYOb0l5T02bNhA9erV6d+/P8899xwJCQk899xzWqtQygfYHXywJdAOaAzEYY0tNdkY83eqbXYC2z0RpPJ+p0+fpk+fPowaNYqyZcuyYMECmjVr5nRYSik3svtY7Xis0WrrGGO2ZLDNz0C2OtypvG3lypWEhYWxb98+OnbsyJAhQyhRooTTYSml3MxuwrjdNRhghowxZ4H+OQ9J5RV//fUXPXr0uDyf9urVq2nQoIHTYSmlPMRuG0Z5Ebk8+KCIPCoik0Wkl2sCJEdpo3fumzdvHpUrV+bzzz/n7bffJjY2VpOFUj7ObsKIBqoBiEg5YC5wM9AZGOiZ0OzTRu/cc+TIEZ5//nmefPJJbrnlFjZs2MAHH3zA9ddf73RoSikPs5sw/ICUEWmfATYYYx4HXgLSHR9K+RZjDFOmTMHf3585c+YwYMAANm/eTHBwpuOVKaV8hN02jIL8M11qI/4ZKHAvcJu7g1Le5cCBA3To0IFFixbxwAMPEB0djb+/v9NhKaVymd0aRhzQUUTqYyWMJa7yssAxTwSmnJecnMwnn3xC5cqVWb16NaNGjeK7777TZKFUPmU3YbwDhAGrganGmJ2u8ieAjR6IK0u00dv9fvrpJxo2bEinTp2oXbs2cXFxvP766zpYoFL5mN2xpGKA0sAtxph2qVZ9itXL21Ha6O0+Fy9e5MMPPyQwMJDY2Fg+//xzvvnmGypUcMuUKEqpPMxuGwbGmEvAn2nK9rs7IOWcHTt2EBISwpYtW3j66aeJiIjg9ttvdzospZSXyNJYUso3nT9/nj59+hAcHMyBAweYMWMGs2bN0mShlLqC7RqG8k3r168nJCSEXbt20bZtW0aMGEGpUqWcDksp5YW0hpFPnTp1im7duvHggw9y+vRpFi9ezMSJEzVZKKUy5BMJQ5+Sypply5ZRpUoVPvroIzp16kRcXBxNmjRxOiyllJfziYShT0nZ8+effxISEkLjxo0pXLgwMTExjB07luLFizsdmlIqD/CJhKEyN2fOHPz9/Zk4cSK9evVix44d1K9f3+mwlFJ5iCYMp+zdC506QYkSUKCA9bNTJ6vcjX7//XeeeeYZWrZsSZkyZdi4cSPvv/8+RYroNOxKqazRhOGExYshMBCiouDkSTDG+hkVZZUvXpzjUxhj+OKLL/D392f+/Pm8//77bNy4kerVq7vhDSil8iNNGLlt715o1QrOnIGkpCvXJSVZ5a1a5aim8csvv9C0aVNefvll/Pz82L59O7169aJQoUI5DF4plZ9pwshtw4dfnSjSSkqCkSOzfOjk5GQiIiIICAjgu+++Y8yYMaxZs4b7778/m8EqpdQ/NGHktsmT7SWMSZOydNjdu3fToEEDunTpwoMPPkh8fDxdunShQAG9xEop99Bvk9x26pRbt0tKSmLIkCFUrVqV+Ph4JkyYwOLFi7nrrrtyEKRSSl3NJ4YGEZEWQItKlSo5HUrmbrjBauC2s10mtm3bRkhICNu2baNVq1aMGTOGMmXKuCFIpZS6mk/UMPJUx70XX4TMGp8LFYKXXspw9blz5/jvf/9LzZo1OXz4MLNmzWLGjBmaLJRSHuUTCSNP6d7dXsJ44410V61du5agoCAGDx5M27Zt2bVrFy1btvRAoEopdSVNGLmtYkWYOROKFr06cRQqZJXPnGltl8rJkyfp2rUr9evX5/z58yxdupTPP/+cm266KReDV0rlZ5ownNC0KcTGQnj4lT29w8Ot8qZNr9h86dKlBAQEEBERQdeuXdm5cyeNGzd2KHilVH7lE43eeVLFijB2rLVk4MSJE7z55ptMnDiR+++/n++++466devmYpBKKfUPrWF4qZkzZ+Ln58eUKVPo3bs327Zt02ShlHKU1jC8zG+//UaXLl2YPXs21atXZ+nSpQQFBTkdllJKaQ3DWxhjGD9+PP7+/ixcuJAhQ4awYcMGTRZKKa+hNQwvsH//fsLDw1m2bBn169cnKiqKe++91+mwlFLqClrDcNClS5cYPXo0AQEBrF+/no8//pjVq1drslBKeSWfqGHkqaFBXHbt2kVISAjr16+nadOmREZGcueddzodllJKZcgnahh5aWiQpKQkBg0aRFBQELt372bSpEksXLhQk4VSyuv5RA0jr9iyZQvt2rUjNjaWZ599ljFjxnDrrbc6HZZSStniEzUMb3f27Fl69uxJ7dq1OXr0KHPmzOGrr77SZKGUylO0huFhMTExhIaG8tNPPxEaGsrQoUO58cYbnQ5LKaWyTGsYHvL333/TqVMnGjRowMWLF1m+fDmfffaZJgulVJ6lCcMDFi1aREBAAJGRkbzxxhvs3LmTRo0aOR2WUkrliN6ScqNjx47xxhtvMHnyZPz9/Vm3bh0PPPCA02EppZRbaA3DDYwxTJ8+HX9/f6ZNm8b//vc/tm7dqslCKeVTtIaRQ4cPH6ZTp07MnTuX4OBgli9fTmBgoNNhKaWU22kNI5uMMURHR+Pv78/SpUsZNmwY69ev12ShlPJZWsPIhn379hEWFsbKlStp0KABUVFR5KVhSZRSKju0hpEFly5dYuTIkQQEBLBp0yY+/fRTVq5cqclCKZUvaA3Dpvj4eEJCQtiwYQPNmjUjMjKScuXKOR2WUkrlGq1hZOLChQu89957VKtWjb179/Lll18yf/58TRZKqXxHaxjXsGnTJkJCQti5cydt2rRh1KhRlC5d2umwlFLKEV5bwxCRh0VkjYhEisjDuXnuM2fO0KNHDx544AFOnDjBvHnzmDJliiYLpVS+lqsJQ0Q+F5EjIhKXpryJiOwWkT0i0tNVbIBTQBHgYG7FuHr1agIDAxk2bBhhYWHEx8fTokWL3Dq9Ukp5rdyuYUwAmqQuEJGCQATQFPAHWouIP7DGGNMUeAfo7+nAEhMTad++PQ0bNgRg5cqVREZGkhcmZVJKqdyQqwnDGBMDnEhTXAvYY4zZZ4y5AEwDnjTGJLvW/wlc58m4Fi9eTOXKlYmKiuKtt94iNjb2cuJQSill8YZG77LAgVSvDwK1RaQl8BhwIzA2o51FJBwIB7I9zemBAwe46aabmD17NrVq1crWMZRSytd5Q8KQdMqMMWY2MDuznY0x44BxAMHBwSY7AYSGhvLKK69QuHDh7OyulFL5gjc8JXUQuCPV63LA4dwMoECBAposlFIqE96QMDYB94hIBREpDDwPzMvKAUSkhYiMS0xM9EiASimlcv+x2qnAeuA+ETkoIiHGmItAF2ApsAuYboyJz8pxjTHzjTHh+kSTUkp5Tq62YRhjWmdQvghYlJuxKKWUyhpvuCWllFIqD/CJhKFtGEop5Xk+kTC0DUMppTzPJxKGUkopz/OGjns5JiItgBbAGRHZlWZ1SSC9e1Vpy28BjnkmwkxlFKOnj2N3+8y2u9Z6u59/RmVOXRenrklW9snudclpuf6tZH87b/1bucvWVsYYn1mAcXbK0isHNntT3LlxHLvbZ7bdtdbb/fyvUebIdXHqmuTGdclpuf6tuP+aZPW6OPW34mu3pObbLLtWuRPcFUtWj2N3+8y2u9b6rHz+ek2ytk92r4u7yp2gfyv2zuMR4spM+Z6IbDbGBDsdh7qSXhfvo9fEO+XGdfG1GkZOjHM6AJUuvS7eR6+Jd/L4ddEahlJKKVu0hqGUUsoWTRhKKaVs0YShlFLKFp/ouOcJIvIwMACIB6YZY1Y7GpBCRApgXZMSWM+cT3Q4JAWISH3gBazvE39jTF2HQ8r3ROROrKmtjwE/GmOGuOO4+aqGISKfi8gREYlLU95ERHaLyB4R6ekqNsApoAjWrIDKA7J4TZ7EmgM+Cb0mHpWV62KMWWOM6QAsADSJe0gW/1buBRYaY9oB/m4LIrd7ajq5AA8B1YG4VGUFgb3A3UBhYIfrAy7gWn8bMMXp2H11yeI16Qm0d20z0+nYfXnJynVJtX46UMLp2H11yeLfSilgFbASeNVdMeSrGoYxJgY4kaa4FrDHGLPPGHMBmAY8aYxJdq3/E7guF8PMV7JyTbBqFX+6trmUe1HmP1m8Lim3QBKNMX/nbqT5RxavyatAX2PMv4Fm7oohXyWMDJQFDqR6fRAoKyItReRTYBLWvUCVe9K9JsBs4DERGQPEOBFYPpfRdQEIAcbnekQqo2uyBHhNRCKB/e46mTZ6g6RTZowxs7G+oFTuy+ianMH6YlLOSPe6ABhj+uZyLMqS0d9KHNDK3SfTGoaVke9I9boccNihWJRFr4l30uvifXL1mmjCgE3APSJSQUQKA88D8xyOKb/Ta+Kd9Lp4n1y9JvkqYYjIVGA9cJ+IHBSREGPMRaALsBTYBUw3xsQ7GWd+otfEO+l18T7ecE108EGllFK25KsahlJKqezThKGUUsoWTRhKKaVs0YShlFLKFk0YSimlbNGEoZRSyhZNGMrricjDImJE5BanY3EXEVkgIhOcjkOprNCEoZTKkIi8IiKnnI5DeQdNGEqlQ0QKiEhBp+NQyptowlCOE5HrRGSUiPwhIudE5HsRqZfOpg+IyHbXNltEpEaqY5QUkUmuGcnOicg+EemWZv041/qTIvKtiASnWv+KiJwSkcddM5pdALqIyAURKZUm3vdFZEeq13VdxzsjIodE5BMRKZFqfVERmeA6/h8i8l+bn8sDIrJSRE6LSKKIrBCRf9n5zNK7jSci5V1lwWm2aSQiG1zxbxaR6inrsYYsL+bazohIP9e6liISKyJnReSE6/3fZud9qbxLE4byBh8CzwHtgGrATmCJiNyeZrthwDtAMLAPWCgiRV3rBgJVgObA/a5jHQIQEQEWYs0T0Nx1jhhgZZpzFAHeBdpjzVo2ATgOPJOygetYrYHJrtdVgG+wBnyrCrQEgoDP08T9KPAfoJHr/A9d6wMRkapYM6btAR4EHsCa0S5lSgK7n5kdg7FmM6zuer9TXO9zHdANOAPc7lqGiUgZrIl6JgJ+rvcyKRvnVXmN09MO6pK/F6AY1v/m26YqS5l2cqDr9cNY8y68kGqbG4C/gFDX63nA+AzO8W+s+dmvT1O+HXjb9fsrrnPUSLPNSGBNqtf1sGb7K+t6/QUQnWafINexbnXFeT6D2Cdc43OZAnzvhs/sllTblHeVBafZ5rFU2zzoKiuX6nM5leb81V3b3OX0vx9dcnfRGoZyWkWgELA2pcAYcwlrVM60k9evT7XNKaz/Vads8wnwrIjsEJFhItIg1X41gKLAUddtoVOuhtwA1/lTXMRKIqlNBh4Ukbtcr18AVhtjDqU69otpjpvyXiq6lsIZxH4t1YAVGazLymdmR2yq31PmUrj1GtvvAJYDcSIyS0Q6ikjpbJxX5TGaMJTTUmYMS2/YZNtDKRtjFgN3Yd3+uQXrdlXKlKEFgD+w/ueferkf6JPqMOddX7ypj7sF+AFoIyKFsG5PTU61SQEgKs1xqwL3YCWf9GZEs+Na+9n5zJLTbAtWkklPUjr7Z/jd4PqMGruWWKxZEH9y3UZTPkwThnLaHqzbK6kbbAsCdYCENNs+kGqbYlg1hF0pZcaYY8aYScaYV7C+xF4WkeuArcBtQLIxZk+a5YiNGKdg1SyaYN0OmpVq3VagcjrH3WOMOet6f0kZxH4tW7FupaXHzmd21PUzdZtGUCbnTM8FrNtdVzCW9caY/kBNrJrJc9k4vspDdE5v5ShjzGkR+QQYIiLHgJ+BN7C+4D9Os/m7InIU68vpf1hfZl8CiMh7WF+y8Vj/rlsC+4wx50VkOdbtm7ki8jZWjaEMVgJYboxZk0mYk4EBrmWeMebvVOs+AL4XkUjgU+AkVs2lhTGmvTHmlIhEAx+kiT2zR3aHuo47DogAzgH1gW+MMb/a+Mz2AAeAfiLSE6v94t1Mzpme/UAREXkU2IbVAB4IPII1ac8fWLfP7uDqBK98jdONKLroAlwHjML68jkPfA/US7X+YaxbJU9g3QI5j5UcaqbapjdWsjgDnAAWAX6p1hcHPsKaA/kC1pfpNKCia/0rpGncTRNjjCuGFumsCwaWAH8Dp7HaJ95Ltb4YVuP4KeAI1m2wBVyj0du1Xz3Xec9iNZIvB26385m5tqmLdVvsLFb7RjPSb/TOsGHcVfYJcMxV3g/ryajFqc69B9fDA7r49qIz7imllLJF2zCUUkrZoglDKaWULZowlFJK2aIJQymllC2aMJRSStmiCUMppZQtmjCUUkrZoglDKaWULZowlFJK2fL/Bsu5Q5BnHtMAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -720,7 +786,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -734,7 +800,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.1" } }, "nbformat": 4, From dfc48f2af9f0abc4a86f221d41aa55ace65ab748 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Mon, 10 Jun 2019 10:44:11 -0400 Subject: [PATCH 08/26] Uncommented some things (oops) --- observing_examples.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index de71d3f..47f2caf 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -369,8 +369,8 @@ " primary_rec=\"P027\", # the suominet receiver nearest APO\n", " sup_rec = []\n", " )\n", - "# new_config.save_to_ecsv('./apache_point.ecsv')\n", - "# settings.import_site_config('./apache_point.ecsv')\n", + "new_config.save_to_ecsv('./apache_point.ecsv')\n", + "settings.import_site_config('./apache_point.ecsv')\n", "\n", "# change site from KPNO to APO\n", "settings.set_site('apache_point')\n", From e1cd58ad60f8c625a18c152cb7bdd880c36ffc12 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Wed, 12 Jun 2019 19:52:06 -0400 Subject: [PATCH 09/26] Added Kepler (ie space-based) example observation of the same stars --- observing_examples.ipynb | 241 ++++++++++++++++++++++++++++++--------- 1 file changed, 187 insertions(+), 54 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 47f2caf..662065e 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# synphot: Predicting count rates with a ground based telescope" + "# synphot: Predicting count rates with ground-based and space-based telescopes" ] }, { @@ -53,14 +53,15 @@ "
  • Model the effect of the quantum efficiency
    • \n", "
  • Combine all of the effects and \"observe\"
    • \n", "
  • Compute the count rate and total counts
    • \n", - "
  • Another example, this time with TRAPPIST-1
    • \n", - "
  • Comparing simulated counts to observed counts
    • \n", + "
  • Look at another example, this time with TRAPPIST-1
    • \n", + "
  • Simluate the observations with a space-based telescope
    • \n", + "
  • Compare simulated counts to observed counts
    • \n", "" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -110,16 +111,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query finished.\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -151,6 +145,13 @@ "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query finished.\n" + ] } ], "source": [ @@ -183,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -260,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -286,12 +287,14 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 8, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -304,11 +307,12 @@ ], "source": [ "sdss = ['r', 'z'] # only want r- and z-bands for now\n", - "apo_sdss_link = 'http://filters.apo.nmsu.edu/data/SDSS/'\n", + "svo_sdss_link = 'http://svo2.cab.inta-csic.es/theory/fps' + \\\n", + " '/getdata.php?format=ascii&id=SLOAN/SDSS.'\n", "\n", "bandpasses = {}\n", "for band in sdss:\n", - " local_path = download_file(apo_sdss_link + band + '.dat')\n", + " local_path = download_file(svo_sdss_link + band + 'prime_filter')\n", " bp = SpectralElement.from_file(local_path)\n", " bandpasses[band] = bp\n", " \n", @@ -327,6 +331,27 @@ "## 5. Model the attenuation by the atmosphere" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Query from Cerro Paranal" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up parameters for the observing night in the format that skyclac_cli would use\n", + "\n", + "# Use the bit from skycalc_cli which queries from Cerro Paranal\n", + "\n", + "# Make a transmission function from it\n", + "# Don't write the output to a file, just let them be arrays" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -362,6 +387,7 @@ "from pwv_kpno import pwv_atm\n", "from datetime import datetime\n", "import pytz\n", + "import os\n", "\n", "# configure the new observation site\n", "new_config = ConfigBuilder(\n", @@ -369,8 +395,9 @@ " primary_rec=\"P027\", # the suominet receiver nearest APO\n", " sup_rec = []\n", " )\n", - "new_config.save_to_ecsv('./apache_point.ecsv')\n", - "settings.import_site_config('./apache_point.ecsv')\n", + "if not os.path.isfile('./apache_point.ecsv'):\n", + " new_config.save_to_ecsv('./apache_point.ecsv')\n", + " settings.import_site_config('./apache_point.ecsv')\n", "\n", "# change site from KPNO to APO\n", "settings.set_site('apache_point')\n", @@ -388,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -431,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -471,12 +498,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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    " ] @@ -504,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -533,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -551,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -559,7 +586,7 @@ "output_type": "stream", "text": [ "HAT-P-11 counts in the r-band, 10 s exposure:\n", - "44167476 cts\n" + "44219273 cts\n" ] } ], @@ -575,7 +602,7 @@ "metadata": {}, "source": [ "\n", - "## Another example, this time with TRAPPIST-1" + "## 9. Another example, this time with TRAPPIST-1" ] }, { @@ -589,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -615,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -654,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -662,7 +689,7 @@ "output_type": "stream", "text": [ "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "308255 cts\n" + "308281 cts\n" ] } ], @@ -684,78 +711,178 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "## 10. Comparing simluated counts to observed counts" + "## 10. Predicting count rates of the same stars, this time observed by a space-based telescope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The space telescope we will use as the example is Kepler.\n", + "\n", + "First query Kepler's response function, which includes both the bandpass and the quantum efficiency:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "kepler_response_path = download_file('https://keplergo.arc.nasa.gov/kepler_response_hires1.txt')\n", + "kepler_response = SpectralElement.from_file(kepler_response_path, wave_unit=u.nm)\n", + "kepler_response.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the observation object. Since Kepler is a space-based telescope, we do not need to convolve with the atmospheric transmission function:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", + " 'trappist1': {'spectrum': photlam_trappist1}\n", + " }\n", + "for star in stars.keys():\n", + " stars[star]['observation'] = Observation(stars[star]['spectrum'], \n", + " kepler_response,\n", + " binset=kepler_response.waveset,\n", + " force='taper')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We know from observations using AGILE on APO's 3.5m telescope that the actual count values for these stars taken on {date} are:" + "Compute the countrates for the two stars:" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "HAT-P-11 in r-band: 34.0 million counts\n" + "synphot count rates:\n", + "\t HAT-P-11: 2.5 million e-/s\n", + "\t TRAPPIST-1 6.5 thousand e-/s\n" ] } ], "source": [ - "hatp11_r_observed = 34000000\n", - "print('HAT-P-11 in r-band:', hatp11_r_observed / 1e6, 'million counts')" + "kepler_area = np.pi * (1.4 * u.m / 2) ** 2 # area of Kepler's primary mirror\n", + "waverange = (kepler_response.waveset[0], kepler_response.waveset[-1])\n", + "\n", + "for star in stars.keys():\n", + " stars[star]['countrate'] = stars[star]['observation'].countrate(area=kepler_area,\n", + " waverange=waverange)\n", + "print('synphot count rates:')\n", + "print('\\t HAT-P-11:', round(stars['hatp11']['countrate'].value / 1e6, 1), 'million e-/s')\n", + "print('\\t TRAPPIST-1', round(stars['trappist1']['countrate'].value / 1e3, 1), 'thousand e-/s')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 10. Comparing simluated counts to observed counts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know from observations using AGILE on APO's 3.5m telescope that the actual count values for these stars taken on certain dates are:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "hatp11_r_apo = 34000000\n", + "hatp11_kepler = 2800000\n", + "trappist1_z_apo = 203000\n", + "trappist1_kepler = 5700" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TRAPPIST-1 in z-band: 203.0 thousand counts\n" + "Ground-based counts at Apache Point Observatory:\n", + "\t HAT-P-11 in r-band: 34 million counts\n", + "\t TRAPPIST-1 in z-band: 203 thousand counts\n", + "Space-based count rates with Kepler:\n", + "\t HAT-P-11: 2.8 million e-/s\n", + "\t TRAPPIST-1 5.7 thousand e-/s\n" ] } ], "source": [ - "trappist1_z_observed = 203000\n", - "print('TRAPPIST-1 in z-band:', trappist1_z_observed / 1e3, 'thousand counts')" + "print('Ground-based counts at Apache Point Observatory:')\n", + "print('\\t HAT-P-11 in r-band:', int(hatp11_r_apo / 1e6), 'million counts')\n", + "print('\\t TRAPPIST-1 in z-band:', int(trappist1_z_apo / 1e3), 'thousand counts')\n", + "print('Space-based count rates with Kepler:')\n", + "print('\\t HAT-P-11:', round(hatp11_kepler / 1e6, 1), 'million e-/s')\n", + "print('\\t TRAPPIST-1', round(trappist1_kepler / 1e3, 1), 'thousand e-/s')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Comparing our expected values to the observed values on a 1-1 line:" + "Comparing our `synphot` values to the observed values on a 1-1 line:" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
    " ] @@ -767,11 +894,17 @@ } ], "source": [ - "plt.scatter(trappist1_z_observed, counts_trappist1, color='r', s=100, label='TRAPPIST1')\n", - "plt.scatter(hatp11_r_observed, counts_hatp11, color='b', s=100, label=\"HATP11\")\n", + "plt.scatter(hatp11_r_apo, counts_hatp11,\n", + " color='b', s=100, label=\"HAT-P-11, ground\")\n", + "plt.scatter(hatp11_kepler, stars['hatp11']['countrate'],\n", + " color='b', marker= '^', s=100, label=\"HAT-P-11, space\")\n", + "plt.scatter(trappist1_z_apo, counts_trappist1,\n", + " color='r', s=100, label='TRAPPIST-1, ground')\n", + "plt.scatter(trappist1_kepler, stars['trappist1']['countrate'],\n", + " color='r', marker= '^', s=100, label='TRAPPIST-1, space')\n", "\n", "# one-to-one line\n", - "plt.plot(np.linspace(1e5, 1e8, 20), np.linspace(1e5, 1e8, 20), color='black')\n", + "plt.plot(np.linspace(1e3, 1e8, 20), np.linspace(1e3, 1e8, 20), color='black')\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", From e10eb6752122131056980b9ed2429656d4881f54 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Wed, 12 Jun 2019 20:01:02 -0400 Subject: [PATCH 10/26] Adjust 1-1 plot size for visability --- observing_examples.ipynb | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 662065e..25ed27f 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -54,7 +54,7 @@ "
  • Combine all of the effects and \"observe\"
    • \n", "
  • Compute the count rate and total counts
    • \n", "
  • Look at another example, this time with TRAPPIST-1
    • \n", - "
  • Simluate the observations with a space-based telescope
    • \n", + "
  • Simluate observations with a space-based telescope
    • \n", "
  • Compare simulated counts to observed counts
    • \n", "" ] @@ -867,24 +867,24 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
    " + "
    " ] }, "metadata": { @@ -894,6 +894,8 @@ } ], "source": [ + "fig = plt.figure(figsize=(9, 9))\n", + "\n", "plt.scatter(hatp11_r_apo, counts_hatp11,\n", " color='b', s=100, label=\"HAT-P-11, ground\")\n", "plt.scatter(hatp11_kepler, stars['hatp11']['countrate'],\n", @@ -914,6 +916,13 @@ "\n", "plt.legend()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 6d163ed89271e9f3b8b5c01574c686d1ffae08b7 Mon Sep 17 00:00:00 2001 From: Tiffany Jansen <33158521+tcjansen@users.noreply.github.com> Date: Thu, 13 Jun 2019 14:58:32 -0400 Subject: [PATCH 11/26] typo fix Co-Authored-By: P. L. Lim <2090236+pllim@users.noreply.github.com> --- observing_examples.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 25ed27f..61be83b 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -54,7 +54,7 @@ "
  • Combine all of the effects and \"observe\"
    • \n", "
  • Compute the count rate and total counts
    • \n", "
  • Look at another example, this time with TRAPPIST-1
    • \n", - "
  • Simluate observations with a space-based telescope
    • \n", + "
  • Simulate observations with a space-based telescope
    • \n", "
  • Compare simulated counts to observed counts
    • \n", "" ] From c8fb9b4c9bd251ee1c1c86a2a82fa62b24f83659 Mon Sep 17 00:00:00 2001 From: Tiffany Jansen <33158521+tcjansen@users.noreply.github.com> Date: Thu, 13 Jun 2019 14:59:11 -0400 Subject: [PATCH 12/26] changed import style Co-Authored-By: P. L. Lim <2090236+pllim@users.noreply.github.com> --- observing_examples.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 61be83b..a9e1a31 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -88,7 +88,7 @@ "\n", "from astroquery.gaia import Gaia\n", "\n", - "import synphot.units as units\n", + "from synphot import units\n", "from synphot.models import Empirical1D\n", "from synphot.spectrum import SourceSpectrum, SpectralElement\n", "from synphot.observation import Observation" From 463965a201ab155df7c5431ce1f602b0c3bf35c2 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Thu, 13 Jun 2019 15:43:25 -0400 Subject: [PATCH 13/26] Addressed pllim's comments: fixed typos, changed variable names, took out binset and forced tapering in Observation calls --- observing_examples.ipynb | 266 ++++++++++++++++++++++----------------- 1 file changed, 149 insertions(+), 117 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index a9e1a31..d195c6b 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -61,8 +61,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 1, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -111,9 +113,18 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 2, + "metadata": { + "collapsed": false + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query finished.\n" + ] + }, { "name": "stderr", "output_type": "stream", @@ -145,13 +156,6 @@ "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query finished.\n" - ] } ], "source": [ @@ -184,8 +188,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 3, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "T_eff = round(T_eff, -2) # round to nearest 100 K\n", @@ -211,19 +217,19 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 4, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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J0yFQjPR5YjHenTgQXWOUkIniQSh3IjV8fqrCqhsZ42RQbpK74RvTISSUWLXOKs/lf/4E\nb6z62mwQRB5S4Z2Iqj4ai0DcavI7m0yHEBLTH77x5LdvbUDLeuno17qe6VCIXK/cJCIi7yJIYxlV\nHe1IRBSRRjXTTIcQkquzmpkOISTjZnyOP/+iBy7r1sR0KESuFuxO5H9jFgVVWlJS9G9FBrSph8/8\nBi2USt7yJCd5Z/aBiTPXYtmXB/Hk2G6mQyFyrWB/0btUdWl5PzGL0KCCo96oVL/v0vYhHXdxhwbl\n7rspwDSyV/V04q6hop4g7nou90beHo61RRREsCTyr6IFEXkrBrG4zs2vemMOika10kL66G1Yq/xH\nXh38xr/6yzU98bOeTSsZWVnq0Q7iTCREgQVLIr6fS62dDsSN1u/xzpTy0a5YH9m1caUfXQXin0T8\n747c3ECAiYSorGBJRMtZJo/r06qusXOr31vprzf0NhRJZDKzc6FevZ0ickCwJHKeiBwRkaMAutnL\nR0TkqIgciVWAFJp66VUrPKZalWQAwNheZes6zklNjnpMgcTD52+rSQuw//ufTIdB5ArlJhFVTVbV\nmqpaQ1VT7OWi9ZBG/BOR4SKyTUTyRSQ7wP57RGSziHwhIotFxDUzHO05/KPpEMKSWT+9wmPuuaQd\n7h7aDlf1KF3X8dCojhjZpXFI56ns06ZgOeTR0Z2DDs7oJgNyPsKT7281HQaRcY79xYpIMoAXAIwA\n0AnWZFad/A5bCyBLVbsBmAPgKafiCdc/V8Rfr+VqqSm4a2hbpPh9UN98QetSTYTvHVYy1PuUMZ3D\nOkfzuucE3V8Y5Fbk+gGZYZ3LtP9bsgPtH0qoAa2JynDya18fAPmqulNVTwGYBb/hUlT1Y1Ut+sr/\nOQDX9ESbtnSH6RCMuePikqHe01NDmbesRLKba8YdcPJMISvcKaE5mUSaAtjjs77X3laem5Bg85QE\ncs8w5yZ8qsjKBy7G0vuGlNpWv4ZV19K0dvA7jFA1qOGNnvXhyszOxU8unzKAyAmueAAtItcCyALw\ndDn7J4hInojkFRTE9yyDN5yfGdbxmfWqRXSeqwL0AWlQMw0t65WuWxncLgMv35CFuy6u3ERURZzo\ne+IWHSe/j8fmbzYdBlFMOZlE9gFo7rPezN5WiogMBfAggNGqGrCLuKrOUNUsVc3KyMhwJFi/8zl+\njvLPHd7x067rFdJxj1/RpdT6M1d3D/kcF3VoWOGwKjN/2RePjq64/iTen3b99ZNdfLxFCcXJJLIK\nQFsRaSUiqQDGAZjne4CI9AAwHVYCOeBgLGF5c/Ve0yGErEOj0KbGjdqHdznlDGhTP6SK8Xho4huK\nzOxcHDlx2nQYRI5zLImo6hkAEwEsBLAFwGxV3SQiU0SkaATgpwFUB/CmiKwTkXnlFBdT98/5wnQI\nUXV9/5YB+4aQs7r97gPcM3ud6TCIHBVe05swqeoCAAv8tk32WR7q5PnJ8uiYLhUf5FI10lJw9IR3\n5z+fu2Yf5q7Zh12/H+nIMDJEprmiYp3Kt/bhYejvwcmR7i2nlVlRxX2orb3evLU/2mRU3JHS7VpN\nWoCFm/5jOgyiqGMS8WP6OXZaldL/JXXSU/GPm/tGVNbUK7tgyW+GRCEqoJXdI75H89ohHX/ZeYEn\nc0pNsa7v7dsG4NUb+1RYjkBiPnDb+ec6k7RveW01K90p7jCJ+Hlt+VdGz181pewYVskRTjhVt1pq\nSMOhhKJXyzr4+DdDcG2/4CPTXNc/EwBQv3pq0OMa1EzDoHZWS7vHruiCrk1rlXtstyD7nNCzRR1H\ny8/MzsXSL+O7qTolDiYRP08v3GY6hGL+c3yEKy3Kgyq2qp9e4XP9mwa2wu6cUaiRViXkcq/r1xLv\n3jEQAJCSXLr8qilJyPlZN/zr9vPDDzhCl3ZuhPrVq6Jf65LRjqdd2zOq57j+5ZW8K6G4wCTiYk18\n6g1mhvlI66aBrTCknfN9aqKtfcMapWZqzKyfjrQqyege4mO0yvos+yJ0aVoLeQ8NxWs3lfybN3Bo\nDvvM7Fw8v3i7I2UTxQKTiIs9+/OSDoEDzq0f1muv7dfSk62BRAS3X3huzM/79//pjd05o0ol7irJ\nSejRwkpeTnZA/cOiL5GZnYsffmK/EvIeJhEXq3VO6I+EIjH3tgH44O5Bjp4jUkM7Nozp+Ya0Dzz/\nfJKdiFXh+DD15z36AbLfiq8+ShT/HO0n4jWJNmOd0xXIlTH9ul44fbbQdBjFHfQLFWhcOw1fHXJ2\nnplZq/Zg1qo9mHplF1zT1zXT6xCVi3ciPg4eO2U6BADA7pxR2J0zqlJl1Hb4LsZpyUmCtCqlGwb8\n/X9iP5Vu0RNBVcUbE/rH7LwPvr0Rmdm5OHgs4HByRK7BJOLjmUXuaZlVGWsfHoY66cGb2HrRkPYN\nsP6RSypdTu1qoSfYonolBdCoVuyHsc96/EO24iJXYxLx8frKPRUfFGVVkkOv/J5vN4OtSDwmkCKB\n6omikVjKU9RFJ9iMjLGQmZ3LZEKuxDoRwzo3qYV1e74P6dguFXS6W3b/hTh20rvjTEXKyQYIgpKK\ndTcoSiSVfdxJFC28EzHskcv9p52PXPO61dCxcWhDw3tZ3kND8eatVv3EmO7W8Crz7xiIu4e2w6oH\nS4/puTtnFBrZfTzOqRJ+58uSOpFKBOyAzOxcjJ/xuekwiJhETCsaS4pCV796VfTOrItp1/bEE1d2\nBWDdpd01tC0yalTFjidGAihJAMsnXYSVD1yMP42z+t20bxj6SAAjujQCALS0Z5D81ZA20bqMSlu+\n8xAys3Nx/csrTYdCCYyfYLbvfzTfMqttg+qmQ3BUjbToPj0d3qUx0quWLTM5STDz5r74930XArAq\nxxvUTMMlnRthd86o4nnjQ3Ftv5bY9OilaF7XSiK/Hd4B+VNHYMKg1tG5iChY+mUBMrNzcdH/LjEd\nCiUg1onYuk9ZZOS8NX3GmGpXybGy3M53OBOnBevhH04/fhEpk6hSkpOQnuq+P52dB4+zzoRijnci\nhhV9wwWAmI957rBQW5PFmheHgwlXUWuuROtAS7HHJEIJJ5op5L9cPu1wq0kLkJmdi59OnTUdCsUp\nJhEXKRrsj5xVdCPy7M/Pi7gMtW8bGxvogBiJjpPfR2Z2Lmbnxb4vFMU39z3YTWDBJmai6Mm0p+it\nl14VT4/tVrmJu0Sw+qGh2FFwHFdPXx6lCJ1z/5wvcP8ca5BH1ptQNDCJADjjgoH+gPhr7tuxcU1c\n07cFvj78I5ZtP2g6nGJ3XHQuerSoXTyzYiSyWta1f9dBvepVUa+61eKrSa007P/hRFTiDObC9hn4\neFvlZkcsqoTf9OilAVu5EYUivj61InS/S4bfDmXipbm3DUC7ht5oCpycJJh6Zdfib/5ukZKcVO7Q\n76Ea2LY+1j9ySalEtPWx4VhiNyt22l1D20WtrM6PLERmdi6unub+OylyHyYRAHPX7DMdAoDQWg31\nbFEHTX0mTiJz/IdbSauSjNSUJMz8ZV9MGtHB0XM7MdPjyt2Hi1t1bf/2aNTLp/jEJGLQ5ec1MR1C\nTAy2v62f1ywxGg4MaFMftwxug9w7yzZxrl+97OCYmfWqldlm2rBn/81mwhQSJhGDHhrV0XQIMTG0\nU0NsfWw4zovRPOlu0blJrTKV163rl30UedblH9JFzYRv/Psq06GQCzGJeJCJOcgry3+CqUTy1q8G\nYP4dA7H1seF4xm5WfP/wkt77heW063B6Ot5wfbT1QPHdycpdh02HQy7hrncphSQrs67pECgMvVrW\nQZemtZBWJRnN6lTD9qkj8KvBbTDt2p4AgJm/7BvwdelVk3FtvxbF68lJoXWTdKK+xN/V05cXJ5Tf\nv7fF8fORezGJGOTypxjkkCrJSRARDO/SGLtzRqFlvXT8zZ76t2ikYQCom56KJJ/GFl+EOPlWqMkm\nWqYv3VmcUG59bXVMz03mOZpERGS4iGwTkXwRyQ6wf5CIrBGRMyIy1slYiNzswvYNsDtnFMZ0b4qV\nD14MAJg3cSA6NCqZH8a/L0e11JJHhL5T/sY6ifh6f9N/ihNKl0cWslI+ATiWREQkGcALAEYA6ARg\nvIj4z8D0NYAbAMx0Ko549cIvemKOPTETxZcGNdKwO2cU0qum4Lzm1igG559br8xxY33G7Vr78LDi\n5RSDScTXsZNniivlM7NzUXD0pOmQyAFOdlPtAyBfVXcCgIjMAjAGwOaiA1R1t73PHV3GPWRUt8am\nQ6AY6NykFp4b3wMXdbA6R+58YmRxa65HLu+MV5d/hTm39i/Vx+iCthmY+ct+rpuTvffUD4uXp13b\nE8O78D0cD5xMIk0B+I72thdA4BpEg746dNx0CERBjfbpT5SUJEiyxyFOTpJSTYi3PjYcc9fswy/6\ntihThr9bBrdGwZGTmLvWTEfbW/+xptT6tseHo2pK4rbg8zJPVKyLyAQRyRORvIKCyo0X5G/w00ui\nWh6RKWlVkkslkOv7tyxe7tOqLq7q0RQA8MHdgzBpREc8ObZb8f7nx/eIXaABtH/o/eLHXi98nM+6\nFA9x8k5kH4DmPuvN7G1hU9UZAGYAQFZWVty8u6pV5Tcvcs7Dl3XC7RediwY1Soarf+bnJa2/qiQn\nYe3Dw5CcLNj+7bFSr90yZTjOsSvuY/1Y7OmF2/D0wm3F67cMao1JIxOjY64XOXknsgpAWxFpJSKp\nAMYBmOfg+Tzlbzf0Lp4aN+eqrph9CyvJKbpSkpNKJZBA6qSnomZaFfjWxe/OGVWcQABgXO+S74I7\nnxhZ6vXX2Hc+C+68APMmnh+FqMua/u+SJsSZ2bmY8u5m3qm4iGN3Iqp6RkQmAlgIIBnAy6q6SUSm\nAMhT1Xki0hvA2wDqALhcRB5V1c5OxWTCR/cOxkV/WFpme/O6JYMojutT8TNsIid1aFQTrTPSkXNV\ntzL7fje6M2at2oNRXUtXhN91cVvcPawdpl7ZNVZhAgBe/nQXXv50V6ltvndOFFuOTiKgqgsALPDb\nNtlneRWsx1xGfL7zkOPnaJ1RHY9f0QUP/Wuj4+ciitQ5qcn46N4hAfelVUnG5imXlqn4vntY2eHo\nn/pZt4BTK9w2pA3+smRHVGINpOPk90utT7+uFy7t3Mix81GJhJ6J5p11+02HQOQJ1VKtj4rCQusx\nUpemNQMeV1jOY6axvZo5mkT83RKg5/zKBy5Gg5remM7YSxI6iSzZdsCRch+7ogse5p0HxaGkJMHs\nW/qXOzFaeVPiuGHWzj5PLC6zbf0jl5SZF4bCk9BJ5PRZZyrnruvXMoQk4o5exUTh6tOq/AFAr+jR\nFL99a0Px+sJfD8Lps4Wo4rIRiYuc9+gHZba9/+sLSg03Q8EldBI5eCx6wzDUTU/F4eOnAu4LYcJC\norhQNSUZuXcOxKjnPsHiewejTYZ1xxLNvzWnDf/jsjLbxnRvgmeu7m50XDK3Stgkkn/gWMUHhaFb\ns1pYsi26HSGJvCjQZFxuGc8rUu+s2x+wDnXatT1xaedGIU1tHa8SNolE+5sRm60TlS9ev8H7D99S\n5Kmx3TC2ZzMkxel1+0rIJHK2UDFuxudGY2hSm61EKHGkpybWR839c77A/XPKNnWuU60KFt87BHXT\nUw1E5YzE+p+13TdnfdTLDOdGJCVJiptMEiWCRPhGHorvfjyNno8tKl73f+znRe5sMuGwuWuiP3Ip\nh2EgokSUkEnEKfPvGIiFvx5UZnuNNLZDJ/r3fReaDoEcwCQSRV2a1kL7RjWK14d1aggAuKwrJ98h\nalGvmukQyAEJl0SOnjjtSLnV/ea/3p0zCi/+dxYA63nw3UNLxhnq27r8zlpE8Sx/6gjTIVCUJVwS\nWb7DmUEXW9ZLD/nYn/fmqL2UmFKSk7B5yqWmw6AoSrgksnLXYdMhcMATSmjVUlOw5uFhpsOgKEm4\nJPL3z3YbOe8F7eobOS+RG9VNT8XunFHIHtHBdChUSQnXWeFMoZmmuD1b1DFyXiI3u3VwG9w6uA2O\nnzyDf674Ck8s2Brya1vXT8djV3RBjxa1sWz7wYDDv5PzEi6JEJH7pFdNwYRBbTBhUJuIXn9p50bY\n8cRILN7yLe6atQ4/nT4b5QipPEwiUZLA468RuUJykuCSzo2w5bHhAKwOwHsO/4T8gqNYt+cHHDhy\nAit2Hcaug8djEk+SAEUPPlJTktCybjXUSEtBetUU9GtdDzcMyIxJHE5jEjGgaHhsInKOiKBFvWpo\nUa8aLurQMCpl/vvLAnRvURt6c7FDAAAMiklEQVQ12YG4WMJVrEdb09rnhP2aTk044Q2RFw1ql8EE\n4odJpBKyWtbBL/qG1+fjgZFsjUJE8SOhksjXh36MankjuzbG4HYZAIChHSu+Xd6dMyriikMiIjdK\nqDqRW/4R/SaAXZqWncWNiChRJNSdyJZvjkS1vK7NakW1PCIir0moO5FoWjd5GGpXi5/ZyYiIIpFQ\ndyLRxARCRJRASeTgsZOmQyAiijsJk0SyHv/QdAhERHHH0SQiIsNFZJuI5ItIdoD9VUXkDXv/ChHJ\ndDIeIiKKLseSiIgkA3gBwAgAnQCMF5FOfofdBOA7VT0XwLMAnnQqHiIiij4n70T6AMhX1Z2qegrA\nLABj/I4ZA+AVe3kOgItFoj+U4TMfbItKOXde3BYdG9cMqWMhEVEicLKJb1MAe3zW9wLoW94xqnpG\nRH4AUA/AwYoKHz/jcxSEUFl++mwhvoqgp/qF7TPw8baCUtvuGdYO9wxrV84riIgSjyf6iYjIBAAT\nAKBFC2usqlYZ6aibXnEzW4WGnURm39IffVrVLSlDFQ7cIBEReZ6TSWQfgOY+683sbYGO2SsiKQBq\nATjkX5CqzgAwAwCysrIUAJ64sqsDIQfGBEJEFJiTdSKrALQVkVYikgpgHIB5fsfMA3C9vTwWwEeq\namb+WiIiCptjdyJ2HcdEAAsBJAN4WVU3icgUAHmqOg/AXwG8JiL5AA7DSjREROQRjtaJqOoCAAv8\ntk32WT4B4L+cjIGIiJyTMD3WiYgo+phEiIgoYkwiREQUMSYRIiKKGJMIERFFTLzWLUNEjgKIzmBY\n7lQfIQz74mHxfH3xfG0Ar8/r2qtqjWgX6olhT/xsU9Us00E4RUTyeH3eFM/XBvD6vE5E8pwol4+z\niIgoYkwiREQUMS8mkRmmA3AYr8+74vnaAF6f1zlyfZ6rWCciIvfw4p0IERG5hKeSiIgMF5FtIpIv\nItmm4ymPiLwsIgdEZKPPtroiskhEttu/69jbRUSes6/pCxHp6fOa6+3jt4vI9T7be4nIBvs1zzkx\npXAF19dcRD4Wkc0isklE7oqnaxSRNBFZKSLr7et71N7eSkRW2DG9YU9xABGpaq/n2/szfcqaZG/f\nJiKX+mw3+l4WkWQRWSsi8+Pw2nbb7511RS2S4uW9aZ+/tojMEZGtIrJFRPobvT5V9cQPrOHkdwBo\nDSAVwHoAnUzHVU6sgwD0BLDRZ9tTALLt5WwAT9rLIwG8B0AA9AOwwt5eF8BO+3cde7mOvW+lfazY\nrx0R4+trDKCnvVwDwJcAOsXLNdrnrG4vVwGwwo5lNoBx9vZpAH5lL98GYJq9PA7AG/ZyJ/t9WhVA\nK/v9m+yG9zKAewDMBDDfXo+na9sNoL7ftrh4b9rnfwXAzfZyKoDaJq8vZhcehX+4/gAW+qxPAjDJ\ndFxB4s1E6SSyDUBje7kxrP4uADAdwHj/4wCMBzDdZ/t0e1tjAFt9tpc6ztC1vgNgWDxeI4BqANYA\n6AurI1qK//sR1pw5/e3lFPs48X+PFh1n+r0Ma5bRxQAuAjDfjjUurs0+526UTSJx8d6ENfvrLtj1\n2W64Pi89zmoKYI/P+l57m1c0VNVv7OX/AGhoL5d3XcG27w2w3Qj78UYPWN/W4+Ya7cc96wAcALAI\n1rfr71X1TICYiq/D3v8DgHoI/7pj5Y8A7gdQaK/XQ/xcGwAogA9EZLWITLC3xct7sxWAAgB/sx9H\nviQi6TB4fV5KInFDrRTv+WZxIlIdwFsAfq2qR3z3ef0aVfWsqnaH9a29D4AOhkOKChG5DMABVV1t\nOhYHDVTVngBGALhdRAb57vT4ezMF1qPy/1PVHgCOw3p8VSzW1+elJLIPQHOf9Wb2Nq/4VkQaA4D9\n+4C9vbzrCra9WYDtMSUiVWAlkH+q6lx7c1xdIwCo6vcAPob1mKa2iBQNFeQbU/F12PtrATiE8K87\nFs4HMFpEdgOYBeuR1p8QH9cGAFDVffbvAwDehvUlIF7em3sB7FXVFfb6HFhJxdz1xfJZZSWfBabA\nqvxphZIKu86m4woSbyZK14k8jdIVX0/Zy6NQuuJrpb29Lqxnn3Xsn10A6tr7/Cu+Rsb42gTAqwD+\n6Lc9Lq4RQAaA2vbyOQCWAbgMwJsoXfl8m718O0pXPs+2lzujdOXzTlgVz654LwMYgpKK9bi4NgDp\nAGr4LH8GYHi8vDft8y+DNZgiAPzOvjZj1xfTN20U/vFGwmoJtAPAg6bjCRLn6wC+AXAa1jeHm2A9\nR14MYDuAD33+wwTAC/Y1bQCQ5VPOjQDy7Z//8dmeBWCj/Zo/w6+SLQbXNxDW7fIXANbZPyPj5RoB\ndAOw1r6+jQAm29tb239g+bA+dKva29Ps9Xx7f2ufsh60r2EbfFq5uOG9jNJJJC6uzb6O9fbPpqLz\nx8t70z5/dwB59vvzX7CSgLHrY491IiKKmJfqRIiIyGWYRIiIKGJMIkREFDEmESIiihiTCBERRYxJ\nhBwlIs+KyK991heKyEs+638QkXuifM5j0SzPLrO7iIz0Wf+diPwmhNeJiHwkIjV9tl0hIioiUe8F\nLyKZIvKLaJfrU/5EEbnRqfLJe5hEyGmfAhgAACKSBKA+rI5qRQbA6hDmdt1h9X8I10gA67X0sDDj\nAXxi/462TAABk4hPj/TKeBnAHVEoh+IEkwg57TNYQ4YAVvLYCOCoiNQRkaoAOgJYIyLVRWSxiKyx\n5zIYAwAikiMitxcV5nsHICL3icgqe56ERwOdPNAx9rf1LSLyoljzhXwgIufY+3rbx64TkadFZKNY\nc2tMAfBze/vP7eI7icgSEdkpIneWc/3XwBrluCie6rA6a94Eqwd40fYhdllF80T8s2geBxEZaW9b\nbc/vUDQHyGA7nnX2YHw1AOQAuMDedreI3CAi80TkIwCL7TujouvaUHQt9vmXisg79vXkiMg1Ys2r\nskFE2gCAqv4IYLeI9KnoP54ShImesvxJrB9YQyq0AHALgFsBPAbrG/r5AJbZx6QAqGkv14fVi1Zg\njRC81KeszbDG/LkE1pzRAuvL0HwAg+xjjtm/Ax4D69v6GQDd7eNmA7jWXt6IkqHPc2APXQPgBgB/\n9onjd7ASZFU73kMAqgS49q9gD8Nhr18D4K/28mcAetnLQ2CNkNvMjnU5rGSTBmu01Vb2ca+jpJf5\nuwDOt5er2/+GQ4r2+8S9FyU9mH8Ga1TiZFgjvX4Na/jvIQC+t5erwhov6VH7NXfBZ4gbWD3V7zX9\nvuKPO354J0Kx8Bmsx1YDYH04LvdZ/9Q+RgA8ISJfwBq2oSms4a3XAmggIk1E5DwA36nqHlgJ4hJY\nw5OsgTXKblu/8wY7ZpeqrrOXVwPIFJHasD7wl9vbZ1ZwXbmqelJVD8Ia8K5hgGPqqupRn/XxsAY+\nhP3b95HWSlXdq6qFsIaSybRj3qmqu+xjXvc5/lMAz9h3QbW1ZCh3f4tU9bC9PBDA62qNUvwtgKUA\netv7VqnqN6p6EtaQFx/Y2zf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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -261,8 +267,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 5, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", @@ -287,32 +295,31 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "sdss = ['r', 'z'] # only want r- and z-bands for now\n", + "sdss = ['rprime', 'zprime'] # only want r' and z' bands for now\n", "svo_sdss_link = 'http://svo2.cab.inta-csic.es/theory/fps' + \\\n", " '/getdata.php?format=ascii&id=SLOAN/SDSS.'\n", "\n", "bandpasses = {}\n", "for band in sdss:\n", - " local_path = download_file(svo_sdss_link + band + 'prime_filter')\n", + " local_path = download_file(svo_sdss_link + band + '_filter')\n", " bp = SpectralElement.from_file(local_path)\n", " bandpasses[band] = bp\n", " \n", @@ -340,8 +347,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 7, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Set up parameters for the observing night in the format that skyclac_cli would use\n", @@ -356,20 +365,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following cells show how to simulate an atmospheric transmission profile at a given site and time of year. We use `pwv_kpno`, a package that downloads data collected by SuomiNet on the precipitable water vapor in the atmosphere on a given night at the given location, and uses them to calculate the transmission as a function of wavelength. The list of sites from which data is collected can be seen here." + "The following cells show how to simulate an atmospheric transmission profile at a given site and time of year. We use `pwv_kpno`, a package that downloads data collected by SuomiNet on the precipitable water vapor in the atmosphere on a given night at the given location, and uses them to calculate the transmission as a function of wavelength. The list of sites from which data is collected can be seen here.\n", + "\n", + "This requires versions > 1.0.1 of `pwv_kpno`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The next cell demonstrates how to configure a `kpno_pwv` observation site other than the default (aka Kitt Peak). Once this cell has been ran, it does not need to be ran again." + "The next cell demonstrates how to configure a `pwv_kpno` observation site other than the default (aka Kitt Peak). Once this cell has been ran, it does not need to be ran again." ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 14, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -377,7 +390,7 @@ "[2018]" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -389,13 +402,13 @@ "import pytz\n", "import os\n", "\n", - "# configure the new observation site\n", - "new_config = ConfigBuilder(\n", - " site_name = \"apache_point\",\n", - " primary_rec=\"P027\", # the suominet receiver nearest APO\n", - " sup_rec = []\n", - " )\n", "if not os.path.isfile('./apache_point.ecsv'):\n", + " # configure the new observation site\n", + " new_config = ConfigBuilder(\n", + " site_name = \"apache_point\",\n", + " primary_rec=\"P027\", # the suominet receiver nearest APO\n", + " sup_rec = []\n", + " )\n", " new_config.save_to_ecsv('./apache_point.ecsv')\n", " settings.import_site_config('./apache_point.ecsv')\n", "\n", @@ -415,24 +428,25 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 19, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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\n", 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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "obsv_date = datetime(year=2018, month=5, day=15) # set the date of observation, let's say mid-May\n", + "obsv_date = datetime(year=2018, month=5, day=15, # set the date of observation, let's say mid-May\n", + " tzinfo = pytz.utc)\n", "transmission = pwv_atm.trans_for_date(date=obsv_date, airmass=1.5) # get the transmission function\n", "\n", "atmosphere = SpectralElement(Empirical1D,\n", @@ -458,25 +472,25 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 47, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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W7+K3b63kglNymTQy/6jl1PQgzSExwbj5zF4s27yXD1cGOsCCSOCiPolU1dTys5cX0yUzlf+67BR1jkuLuGRoHnmZaaqNSNyL+iTywpwNrCk9wF0XDiQjNTnocCROJCcmcNM3elK0fjfzN2gqXYlfUZ1E9h+q5g/vfsGIHh0456ROQYcjcebSYV1pm5LEs7PXBx2KSGCiOok8/NFqdh6o5BfjT1IzlrS4tilJfHtoF6Yv3tKo4XVEYlHUJpGtZRU88skaLhyUy5BumUGHI3Fq0sjuVFbX8sr8kqBDEQlE1CaRP7y7ippaxx3n9Q86FIljJ+VmMLx7e56dvYHaWnWwS/yJyiSypewgLxeVcPWIfE1hK4G7ZlQ+a3ccYObqnUGHItLiojKJPDFjHbXO8b3TewYdigjnn5xL+9bJ6mCXuBR1SaTWOZ6bvYHxp+QeMbSJSFBSkxP5l8JuvL18G1vLNBe7xJeoSyI7D1Sy71A1N56hWohEjkkj86l1TrURiTvRl0T2VzKqZwcGddUdWRI5undswzf7d+K52Rs4VK0pdCV+RF0Sqaqp5aYzegUdhsjXXDemBzsPVDJ90ZagQxFpMVGXRFKSEjizX3bQYYh8zdjeHendqS1PzFyn8bQkbkRdEslOT9HT6RKRzIzJYwpYsqmM+Rt2Bx2OSIuIuiSSmdYq6BBEjurSoXmkpybx+Ix1QYci0iKiLomoEiKRrE1KElcWduPNpVvZUnYw6HBEfBd1SUQk0k0eU4Bzjqdm6XZfiX1KIiLNrFuH1pw3MIfnZm+gvLI66HBEfKUkIuKD753eg7KDVbxSpNF9JbYpiYj4YFh+e4Z0y+SxT9dqdF+JaUoiIj4wM753eg/W7SznvRXbgw5HxDdKIiI+GTcwh7zMNB79ZE3QoYj4RklExCdJiQlcN6aA2Wt3sXRTWdDhiPjC1yRiZuPMbKWZFZvZnUcpc4WZLTezZWb2nJ/xiLS0K0d0Iy05kWc+0+2+Ept8SyJmlgg8AJwPDAAmmtmAOmX6AD8HxjrnBgI/9isekSBkpCZz8eAuTFu0mX0VVUGHI9Ls/KyJjACKnXNrnHOVwAvAhDplbgAecM7tBnDOqQdSYs7VI/Mpr6zhbws3Bx2KSLPzM4nkARvD1ku8beH6An3NbIaZfWZm43yMRyQQg7q2Y2CXDJ79bL1G95WY42cSqW+Uq7r/g5KAPsCZwETgUTP72mxTZnajmc0zs3mlpaXNHqiIn8yMq0fms2LrPhZs3BN0OCLNys8kUgJ0C1vvCtStz5cAf3fOVTnn1gIrCSWVIzjnpjrnCp1zhdnZmktEos+EIXm0aZXIc7M3BB2KSLNqVBIxs15mluItn2lmP6yvxlDHXKCPmfUws1bAVcC0OmX+BpzlHTeLUPOWbqqXmNM2JYkJQ/P4x6LNlJWrg11iR2NrIq8ANWbWG3gM6AE0eDuuc64auBV4C/gceNE5t8zM7jazi71ibwE7zWw58AHwM+fcziach0jEmzQyn0PVtbxUtPHYhUWihDWmo8/M5jvnhpnZz4AK59yfzGyBc26o/yEeqbCw0M2bN6+l31akWVz+4ExK9x/ig9vPJCFBk+NIyzGzIudcYXMft7E1kSozmwhMBqZ725KbOxiRWHftmALW7yzno1W6QURiQ2OTyPXAaOA/nXNrzawH8Ix/YYnEpnEDc8hOT+HJWeuCDkWkWTQqiTjnljvnfuice97M2gPpzrl7fI5NJOa0Skpg0sh8PlxZyrodB4IOR+SENfburA/NLMPMOgCLgMfN7Pf+hiYSm64ekU9Sgmn6XIkJjW3Oauec2wtcCjzunBsOnONfWCKxq1NGKuNPyeWloo0cOKTpcyW6NTaJJJlZLnAFX3Wsi0gTTR7TnX0V1by+ZEvQoYickMYmkbsJPdOx2jk318x6Al/4F5ZIbBuW3578Dq15fbGSiES3xnasv+ScG+Scu9lbX+Ocu8zf0ERil5kx/pRcZhTvYPeByqDDEWmyxnas9zWz98xsqbc+yMz+3d/QRGLbhYNyqa51vL18a9ChiDRZY5uzHiE0eVQVgHNuMaGxsESkiQZ2yaB7x9ZMV5OWRLHGJpHWzrk5dbbpthKRE2BmXHBKLjNX72SXmrQkSjU2iewws15484GY2eWAvj6JnKALBuVSU+t4a5matCQ6NTaJ/AB4GOhvZpsIzYV+s29RicSJAbkZ9Mhqo7u0JGo19u6sNc65c4BsoL9z7jTn3DpfIxOJA181ae1g5/5DQYcjctySGtppZrcdZTsAzjkNfSJygi4YlMv9HxTzz2VbmTSye9DhiByXY9VE0ht4tfU3NJH40D8nnYKOrfnnUvWLSPRpsCbinPs1gJmNdc7NCN9nZmP9DEwkXpgZ552cw2OfrKXsYBXt0jRVj0SPxnas/6mR20SkCcYNzKG61vH+im1BhyJyXI7VJzIaGANk1+kfyQAS/QxMJJ4M7ppJ54wU/rl0K5cM7Rp0OCKNdqyaSCtCfR9JHNkfshe43N/QROJHQoJx3sAcPlpVysHKmqDDEWm0Y/WJfAR8ZGZPOOc0g46Ij8YNzOGpWev5aFUp407OCTockUY5VnPWH5xzPwbuNzNXd79z7mLfIhOJMyN6dCCzdTJvLduqJCJRo8EkAjzt/fyd34GIxLukxATOOakzby/bSmV1La2SGnvfi0hwjtWcVeT9/KhlwhGJb+MG5vByUQmz1uzkG32zgw5H5JgaO5/IWDN7x8xWmdkaM1trZmv8Dk4k3pzWJ4vM1sn8de6GoEMRaZRjNWcd9hjwE6AI0K0jIj5JTU7kysJuPPrpWraUHSS3XVrQIYk0qLGNrmXOuTedc9udczsPv3yNTCROXTOqO7XO8dxs1UYk8jU2iXxgZr81s9FmNuzwy9fIROJUtw6t+Wb/zjw/ZwOHqlXxl8jW2Oaskd7P4d5PIzRB1dnNHpGIMHlMd959bBtvLNmiJ9glojV2KPjp3k8HlAKfOufW+hmYSDwb2yuLntlteHLmeiURiWiNHQq+rfdKBwqBN83sKp9jE4lbCQnGtaO6s3DjHhZt3BN0OCJH1aih4Osysw7Au8ALDf2+mY0D/pfQYI2POufuOUq5y4GXgFOdc/MaEbdIzLtseFf+680VvDK/hMHdMoMOR6ReTXok1jm3i1C/yFGZWSLwAHA+MACYaGYD6imXDvwQmN2UWERiVXpqMmf168SbS7dSU/u1UYdEIkKTkoiZnQ3sPkaxEUCxNz97JaFay4R6yv0G+G+goimxiMSyCwblUrrvEPPW7Qo6FJF6HatjfQmhzvRwHYDNwLXHOHYesDFsvYSv7vI6fPyhQDfn3HQz+2kDcdwI3AiQn59/jLcViR1n9+9ESlICry/ZwsieHYMOR+RrjnWL74V11h2w0zl3oBHHrq+568uEZGYJwH3Adcc6kHNuKjAVoLCwUPV6iRttUpI4u3+oSes/LhpIYkKDrcgiLa7B5izn3Po6rw2NTCAQqnl0C1vvSqgGc1g6cDLwoZmtA0YB08yssPHhi8S+8aeEmrTmqklLIpCfY03PBfqYWQ8zawVcBUw7vNM5V+acy3LOFTjnCoDPgIt1d5bIkc7u34nU5ATeWLIl6FBEvsa3JOKcqwZuBd4CPgdedM4tM7O7zUyTWYk0UpuUJN2lJRGrscOeNIlz7g3gjTrb7jpK2TP9jEUkml0wKJc3l25l7rpdjFIHu0QQTZ0mEgUON2lNX7z52IVFWpCSiEgUaN0qiW8NyGH64i1UVtcGHY7Il5RERKLEpcPy2FNexQcrtwcdisiXlEREosTpvbPIapvCa/M3BR2KyJeURESiRFJiAhOGdOG9FdvYU14ZdDgigJKISFS5ZGgeVTWO6Yv1zIhEBiURkSgysEsG/Tqn89oCNWlJZFASEYkiZsYlw/IoWr+bdTsaOwKRiH+URESizLeH5GEGr84vCToUESURkWiT0y6V0/tk83JRiYZBkcApiYhEoSsLu7G5rIIZxTuCDkXinJKISBQ6Z0AnMlsn8+K8jccuLOIjJRGRKJSSlMi3h+Tx9jI9MyLBUhIRiVJXFHajsqaWv+l2XwmQkohIlBrQJYNT8trx4jzdpSXBURIRiWJXFHZl+Za9LN1UFnQoEqeURESi2MVD8khJSuD5ORuCDkXilJKISBRrl5bMRYO78NqCTeytqAo6HIlDSiIiUW7y6ALKK2t4pUh9I9LylEREotwpXdsxLD+Tp2atp1ZPsEsLUxIRiQGTxxSwdscBPtET7NLClEREYsD5J+eS1TaFJ2euCzoUiTNKIiIxoFVSAlePzOeDldtZv1NDxEvLURIRiRGTRuaTaMbTs9YHHYrEESURkRjROSOV8wbm8PL8EiqqaoIOR+KEkohIDLl6ZD57yqt4c6nmYJeWoSQiEkNG9+xIQcfWPDdbT7BLy1ASEYkhCQnGxBH5zF23m1Xb9gUdjsQBJRGRGHP58K4kJ5pqI9IilEREYkzHtimcNzCHV9XBLi3A1yRiZuPMbKWZFZvZnfXsv83MlpvZYjN7z8y6+xmPSLy4emQ+eyuqeX2xOtjFX74lETNLBB4AzgcGABPNbECdYguAQufcIOBl4L/9ikcknozu2ZFe2W145JM1Gk9LfOVnTWQEUOycW+OcqwReACaEF3DOfeCcK/dWPwO6+hiPSNwwM374zT6s2LqPfyzeHHQ4EsP8TCJ5wMaw9RJv29FMAd70MR6RuHLRoC70z0nnvndWUVVTG3Q4EqP8TCJWz7Z669Vmdg1QCPz2KPtvNLN5ZjavtLS0GUMUiV0JCcbt5/Zj3c5yzTUivvEziZQA3cLWuwJfq1eb2TnAL4GLnXOH6juQc26qc67QOVeYnZ3tS7AiseickzoxuFsmf3zvC92pJb7wM4nMBfqYWQ8zawVcBUwLL2BmQ4GHCSWQ7T7GIhKXzIw7zuvH5rIKPTcivvAtiTjnqoFbgbeAz4EXnXPLzOxuM7vYK/ZboC3wkpktNLNpRzmciDTR2N5ZjOrZgakfr1HfiDS7JD8P7px7A3ijzra7wpbP8fP9RSTkpjN6cf0Tc3ljyRYmDGno/haR46Mn1kXiwDf6ZtPTe27EOT03Is1HSUQkDiQkGFNO68HSTXuZs3ZX0OFIDFESEYkTlw7tSvvWyTz26dqgQ5EYoiQiEifSWiVyzajuvPP5Ntbt0Dzs0jyURETiyHdGdScpwXj00zVBhyIxQklEJI50ykjl8uHdeOazDdz//hfqZJcT5ustviISeX598UAqqmr43dur2FxWwd0XDyQpUd8npWmURETiTKukBH5/xWBy2qXy4Ier2b63gj9OHErrVvo4kOOnrx8iccjM+Ldx/fnNhIG8v2I7Ex+ZzY799Q5dJ9IgJRGROPad0QU8dM1wVmzZy2UPztRdW3LclERE4ty5A3N47oZR7D1YxWUPzmThxj1BhyRRRElERBjevT2v3DyG1imJXDV1Fu99vi3okCRKKImICAA9s9vy6s1j6dMpnRuemqeh46VRlERE5EvZ6Sm8cOMozuibzS9eW8L/vL1Sz5JIg5REROQIbVKSeOTaQq4o7Mqf3i/mpy8t1jwkclS6MVxEviY5MYF7LxtEl8w0/vDuF2zfV8GD1wynbYo+MuRIqomISL3MjB+f05d7LzuFmat3cuXDs9i+tyLosCTCKImISIOuPDWfRycXsnbHAS7580yKt+8LOiSJIEoiInJMZ/XrxAs3juJQdQ2XPTiLues0sZWEKImISKMM6prJqzePpUObVkx6dDZvLtkSdEgSAZRERKTR8ju25pWbx3BylwxueW4+j8/QLInxTklERI5LhzatePZ7o/jWSZ359T+W8//e+JzaWj1LEq+URETkuKW1SuTBa4bznVHdmfrxGn7014Ucqq4JOiwJgG76FpEmSUww7p4wkC6Zadz7zxWU7qvg4e8U0i4tOejQpAWpJiIiTWZm3HxmL+67cjBF63dzxUOz2LznYNBhSQtSEhGRE3bJ0K48cf0INu05yKV/nsmKrXuDDklaiJKIiDSLsb2zePGm0Tgc//LgLGYW7wg6JGkBSiIi0mwGdMng1VvGktMulcmPz+HvCzcFHZL4TElERJpVXmYaL39/DMPy2/OjFxby0EeUDe2XAAAMlUlEQVSrNZx8DFMSEZFm1651Mk9NGcEFg3K5580V/GraMmr0LElM0i2+IuKLlKRE/nTVUHIzUnn007Vs3VvB/141lNTkxKBDk2bka03EzMaZ2UozKzazO+vZn2Jmf/X2zzazAj/jEZGWlZBg/PuFA/g/Fw7g7eXbmPTobHYfqAw6LGlGviURM0sEHgDOBwYAE81sQJ1iU4DdzrnewH3AvX7FIyLBmXJaD+6fOIwlm8q47KGZbNxVHnRI0kz8rImMAIqdc2ucc5XAC8CEOmUmAE96yy8D3zQz8zEmEQnIBYNyeWbKSHbsO8Qlf57J0k1lQYckzcDPJJIHbAxbL/G21VvGOVcNlAEdfYxJRAI0okcHXrl5DClJCVzxsJ4liQV+JpH6ahR1b89oTBnM7EYzm2dm80pLS5slOBEJRp/O6bx6yxiGd29PXvu0oMORE+RnEikBuoWtdwU2H62MmSUB7YCvTZnmnJvqnCt0zhVmZ2f7FK6ItJTOGak8PWUk3Tu2CToUOUF+JpG5QB8z62FmrYCrgGl1ykwDJnvLlwPvOz2VJCISNXx7TsQ5V21mtwJvAYnAX5xzy8zsbmCec24a8BjwtJkVE6qBXOVXPCIi0vx8fdjQOfcG8EadbXeFLVcA/+JnDCIi4h8NeyIiIk2mJCIiIk2mJCIiIk2mJCIiIk2mJCIiIk1m0fZYhpmVAusDevssIJ7GaYi38wWdc7yIx3Pu55xLb+6DRt18Is65wB5ZN7N5zrnCoN6/pcXb+YLOOV7E6zn7cVw1Z4mISJMpiYiISJMpiRyfqUEH0MLi7XxB5xwvdM7NJOo61kVEJHKoJiIiIk0W90nEzBLNbIGZTffWe5jZbDP7wsz+6g1jj5mleOvF3v6CsGP83Nu+0szOC+ZMGs/MMs3sZTNbYWafm9loM+tgZu945/2OmbX3ypqZ/dE7v8VmNizsOJO98l+Y2eSjv2OwzOwnZrbMzJaa2fNmlhqL19nM/mJm281sadi2ZruuZjbczJZ4v/PHoKeyPsr5/tb7u15sZq+ZWWbYvnqvn5mN87YVm9mdYdvr/RsJUn3nHLbvp2bmzCzLW2+Za+yci+sXcBvwHDDdW38RuMpbfgi42Vu+BXjIW74K+Ku3PABYBKQAPYDVQGLQ53WMc34S+J633ArIBP4buNPbdidwr7c8HniT0CyUo4DZ3vYOwBrvZ3tvuX3Q51bPueYBa4G0sOt7XSxeZ+AMYBiwNGxbs11XYA4w2vudN4HzI/B8zwWSvOV7w8633uvnvVYDPb3/C4uAAWF/K1/7G4m0c/a2dyM07cZ6IKslr3Hgf/gBX5CuwHvA2cB07x9uR9gf4WjgLW/5LWC0t5zklTPg58DPw475ZblIfAEZhD5Urc72lUCut5wLrPSWHwYm1i0HTAQeDtt+RLlIeRFKIhu9/zBJ3nU+L1avM1BQ50O1Wa6rt29F2PYjykXK+dbZdwnwrLdc7/ULv/bh5Rr6LAj6Vd85Ay8Dg4F1fJVEWuQax3tz1h+AO4Bab70jsMc5V+2tlxD6EIKvPozw9pd55b/cXs/vRKKeQCnwuIWa8R41szZAZ+fcFgDvZyev/NHOLyrO2zm3CfgdsAHYQui6FRH71/mw5rqued5y3e2R7LuEvk3D8Z9vQ58FEcXMLgY2OecW1dnVItc4bpOImV0IbHfOFYVvrqeoO8a+hn4nEiURqg4/6JwbChwg1MxxNFF93l4fwARCTRhdgDbA+fUUjbXrfCzHe55Rdf5m9kugGnj28KZ6ikX9+ZpZa+CXwF317a5nW7Ofc9wmEWAscLGZrQNeINSk9Qcg08wODwfTFdjsLZcQanfE29+O0JS+X26v53ciUQlQ4pyb7a2/TCipbDOzXADv5/aw8vWdX7Sc9znAWudcqXOuCngVGEPsX+fDmuu6lnjLdbdHHK+j+EJgkvPaZTj+893B0f9GIkkvQl+QFnmfZV2B+WaWQwtd47hNIs65nzvnujrnCgh1oL7vnJsEfABc7hWbDPzdW57mrePtf9/7A50GXOXd1dMD6EOocyoiOee2AhvNrJ+36ZvAco48v7rnfa13p8cooMxrFnkLONfM2nvf9s/1tkWaDcAoM2vt3Wly+Hxj+jqHaZbr6u3bZ2ajvH/Ha8OOFTHMbBzwb8DFzrnysF1Hu35zgT7enVitCH0WTPOu+dH+RiKGc26Jc66Tc67A+ywrAYZ5/89b5hoH3UkUCS/gTL66O6snoT+uYuAlIMXbnuqtF3v7e4b9/i8J3eGxkoDvWGnk+Q4B5gGLgb8RukOjI6GbDL7wfnbwyhrwgHd+S4DCsON81/v3KAauD/q8GjjfXwMrgKXA04Tu0Im56ww8T6jfp8r7MJnSnNcVKPT+DVcD91Pn5owIOd9iQu39C73XQ8e6foTuYlrl7ftl2PZ6/0Yi7Zzr7F/HVx3rLXKN9cS6iIg0Wdw2Z4mIyIlTEhERkSZTEhERkSZTEhERkSZTEhERkSZTEhFfmdl9ZvbjsPW3zOzRsPX/MbPbmvk99zfn8bxjDjGz8WHrvzKznzbi98zM3jezjLBtl3ijrfb3Ic4CM7u6uY8bdvxbzex6v44v0UdJRPw2k9AT4phZApAFDAzbPwaYEUBcx2sIoecJjtd4YJFzbm/YtonAp4QebGtuBUC9SSTs6esT8Rfgh81wHIkRSiLitxl4SYRQ8lhK6KnY9maWApwELDCztmb2npnN9+YzmABgZvea2S2HD+bVAG73ln9mZnO9uRJ+Xd+b11fG+7b+uZk9YqF5Rt42szRv36le2VkWmptiqfck893AlWa20Myu9A4/wMw+NLM1Zna0D9ZJhD31a2ZtCQ25M4WwJGJmZ3rHOjzPy7PeU8OY2Xhv26cWmuPh8Nw33/DiWWihwTTTgXuA071tPzGz68zsJTP7B/C2VzM6fF5LDp+L9/4fmdmLZrbKzO4xs0lmNscr1wvAhZ4CX2dmI4514SVOBP0Epl6x/yL0FG0+cBPwfeA3hL6hjwU+9sokARnechahJ2kNGAp8FHas5d6xziU0Z7QR+jI0HTjDK7Pf+1lvGULf1quBIV65F4FrvOWlwBhv+R68IbcJzUFyf1gcvyJUy0rx4t0JJNdz7uuB9LD1a4DHvOWZhIaogNCoCWWExitKAGYBpxF6gn4j0MMr9zxfja7wD2Cst9zW+zc88/D+sLhL+OpJ9cuAdwjNo9GZ0LAwud7v7fGWU4BNwK+93/kR8IewY/4SuD3ovyu9IuOlmoi0hMO1kTGEPhxnha3P9MoY8P/MbDHwLqEhqDs75xYAncysi5kNBnY75zYQShDnAguA+UB/QuMhhWuozFrn3EJvuQgosNAseOnOucMxPXeM83rdOXfIObeD0MCGnesp08E5ty9sfSKhAT/xfk4M2zfHOVfinKslNGRHgRfzGufcWq/M82HlZwC/92pBme6rYcvresc5t8tbPg143jlX45zbBnwEnOrtm+uc2+KcO0Ro2Iu3ve1LvFgO205oRGQRmqONVORYDveLnELom/5G4HZgL6E2dgg1+2QDw51zVRYakTTV2/cyoYHwcvjqA9iA/3LOPdzA+9ZbxkJT3h4K21QDpFH/UNgNqXuM+v4/VZtZgnOu1sw6Ehot+mQzc4RqA87M7mjgeEeNyTl3j5m9TqhW95mZnXOUogfClhs6x/D3rw1br+XIc0sFDjZwHIkjqolIS5hBaGjuXd434F2EpuQdTahWAqEh17d7CeQsoHvY779AqP/gckIJBUIjkX7X62PAzPLMrBNHakyZLznnduONYuptCu/43gekH89Je1YSGsgPL/6nnHPdXWjU1W6EZpk8rYHfXwH0tK/mej/cH4OZ9XKhUVzvJTSgZv9GxPkxob6dRDPLJtS8d7yjEfcl9GVARElEWsQSQv0Gn9XZVuY1BUFo8qBCM5tHqFay4nBB59wyQh+Mm9xXs/S9Tai5aZaZLSGUXI748GxMmXpMAaaa2SxC39rLvO0fEOpID+9Yb4zXCfU3QKjp6rU6+1/hKHdTeedwkNC87/80s0+BbWEx/djrIF9EqGbwJqGRmavNbJGZ/aSeQ77mlVkEvA/c4ULDhh+PsYSaHEU0iq9IODNr65zb7y3fSWh+8h+dwPFyCdU+vnWiMXl3az0AfOGcu6+pxzsRZjYUuM05950g3l8ij2oiIke6wKttLAVOB/7viRzMqzk9YmEPGzbBDWa2EFhGqNmvoX4gv2UB/yfA95cIo5qIiIg0mWoiIiLSZEoiIiLSZEoiIiLSZEoiIiLSZEoiIiLSZEoiIiLSZP8fBLtGr0tgACkAAAAASUVORK5CYII=\n", + "image/png": 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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "quantum_efficiency = SpectralElement.from_file('ccd_QE.csv', wave_unit=\"nm\")\n", - "quantum_efficiency._model.lookup_table /= 100 # convert percentages to decimals\n", + "quantum_efficiency = quantum_efficiency / 100 # convert percentages to decimals\n", "\n", "quantum_efficiency.plot(left=3000, right=14000)" ] @@ -498,19 +512,19 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 48, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -519,7 +533,7 @@ "for band in sdss:\n", " bp_atmos_qe[band] = bandpasses[band] * atmosphere * quantum_efficiency\n", " \n", - "bp_atmos_qe['r'].plot(left=3000, right=14000)" + "bp_atmos_qe['rprime'].plot(left=3000, right=14000)" ] }, { @@ -531,16 +545,15 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 75, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ - "band = 'r' # only want to know about the r-band\n", + "band = 'rprime' # only want to know about the r' band\n", "\n", - "observation = Observation(photlam_hatp11, \n", - " bp_atmos_qe[band],\n", - " binset=bp_atmos_qe[band].waveset,\n", - " force='taper')" + "observation = Observation(photlam_hatp11, bp_atmos_qe[band])" ] }, { @@ -560,13 +573,13 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": 63, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ - "waverange = (3000, 11300) # according to the ARCTIC webpage, the z band is cut off around 11k AA\n", - "\n", - "countrate = observation.countrate(area=aperture_area, waverange=waverange) / gain" + "countrate = observation.countrate(area=aperture_area) / gain" ] }, { @@ -578,15 +591,17 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "execution_count": 64, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HAT-P-11 counts in the r-band, 10 s exposure:\n", - "44219273 cts\n" + "44043008 cts\n" ] } ], @@ -616,11 +631,13 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": 82, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ - "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", + "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", "\n", "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", " 'PHOENIX-ACES-AGSS-COND-2011/Z-0.0/lte{T_eff:05d}-{log_g:1.2f}-0.0.PHOENIX-'\n", @@ -642,21 +659,20 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 83, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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ITqzcKH3HWSfhtRns5unNGzM3edxfeLwUdRPjISIBnU9VUVKm1do5fvfKHADA9YPTgoqT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\n3f4gAuAN875WAUh3O89NADa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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -667,7 +683,7 @@ "# Scale the flux to get the value at Earth\n", "stellar_radius = 0.117 * u.R_sun # radius of trappist-1 in solar units\n", "distance = 12.1 * u.pc # distance to trappist-1 in parsecs\n", - "photlam_trappist1 = photlam_trappist1 * float(stellar_radius / distance) ** 2 / np.pi\n", + "photlam_trappist1 = photlam_trappist1 * (stellar_radius / distance).value ** 2 / np.pi\n", "\n", "photlam_trappist1.plot(flux_unit='Jy', left=0, right=60000)" ] @@ -681,20 +697,26 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "execution_count": 84, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "308281 cts\n" + "522921278184389214208 cts\n" ] } ], "source": [ - "band = 'z'\n", + "band = 'zprime'\n", + "# observation = Observation(photlam_trappist1, \n", + "# bp_atmos_qe[band],\n", + "# binset=bp_atmos_qe[band].waveset,\n", + "# force='taper')\n", "observation = Observation(photlam_trappist1, \n", " bp_atmos_qe[band],\n", " binset=bp_atmos_qe[band].waveset,\n", @@ -725,19 +747,19 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 85, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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MbN59kIK9ZZwxINXrUEwriY6M4NHLTmbxXWdz7uAuPPjOWm6dtYzSimqvQzNtiCWRMPHZBmfgytP629hj4aZL+zievHIkd5w7gLkrtnHDS0s5WGmJxLQMSyJh4rP1u8hIaUcvG+IkLEVGCD8/uz9/vmIYX27aw9T/XcTWfWVeh2XaAEsiYaC0opoFuUVMGNDZrsoKc1OG9eC5a0eRv+cgUx5fwFeb93odkglxlkTasJUFxcxevo2/L95MeVUtFw61OUMMnHlCZ/5106kkxEbx41eyyd9z0OuQTAizJNJGfb6hiCkzF3DLa9/w+/fWMb5fKqdk2syFxtGvcxJP/HAE5VW1THzsM1YWFHsdkglRlkTaqIffz6FXpwSevTqLey4azJNXjbSmLHOEE7t34P3bTqN9u2imP7OYuStsWHlz7CyJtEGFJeWs3FrM1JHpnDO4C9ePzyQxNsrrsEwQSk+O5/Ufj6V/l0R++cZyVm+zGok5NpZE2qBPcpzLee3OdNMcGSnxPHnlSFLiY7jm+S/ZtKvU65BMCLEk0gbNzymka/s4BnZN8joUEyK6tI/jlR+dgipc9uQiPskp9DokEyIsibQxVTW1fL5hF2cOTLM+EHNM+qYl8vINo4mJFG775zK7ass0iyWRNiY7by8HKqqZYEO9Gz+c2L0DL99wCrW1ypXPLbFJrkyTAppERGSiiOSISK6I3NnA/mtFpEhElrmPHwUynnDwSU4h0ZHCuH42RpbxT7/Oibxw3Wi27yvnjjeW26CNplEBSyIiEgnMBCYBg4HpIjK4gaL/VNVh7uPZQMUTDmpqlbkrtjOmTye7Gsscl5G9krlz0kA+XlfIS1/keR2OCWKB/KYZDeSq6kYAEZkFTAHWBPA9w9Irizcze9lWoiMj2LqvjLsvHOR1SKYNuHpsL95ZuZ1756yhY3wMFw/v4XVIJggFsjmrB5Dvs17gbqvv+yKyQkTeFJGMAMbTJi3L38dv3l7Ft0WlLNm0h6vH9mLSkK5eh2XagKjICF67cQyjM1P477dWsKJgn9chmSAUyCTS0KVB9RtX5wC9VXUo8CHwUoMHEpkhItkikl1UVNTCYYa25xdsomN8NJ/96kw2PDiJ+6cMsauyTIuJiYrgf384gtTEWGa8/BWF+8u9DskEmUAmkQLAt2aRDhwxroKq7lbVCnf1GWBkQwdS1adVNUtVs9LSbD6MOtU1tXy6vohzBnUhMTaKiAhLHqbldUqM5Zmrs9hXVskv31hBeVWN1yGZIBLIJLIU6C8imSISA0wDZvsWEBHfYWUnA2sDGE+bs7xgH8VlVUw4wRKrCazB3dtz16RBfLa+iDMemc83W2wIeeMIWBJR1WrgZmAeTnJ4XVVXi8j9IjLZLXaLiKwWkeXALcC1gYqnLfo0p4gIgdP6WRIxgXfNqb157cYxxEZFcsVTi/ntO2uorqn1OizjMQm1a8CzsrI0Ozvb6zCCwuTHFxATGcGbPz3V61BMGNlbWsk9s1czZ/k2+qQm8JMz+vL9kelEWnNqUBORr1Q1q6WPa3esh6jC/eWsKCjmjAFWCzGtKzkhhr9NH87vLjkJgF+9tYLTH57P7a8vI88Gbww7lkRC1DsrtgMw0S7nNR75wSk9+eiOM/jLtGHERkUwd/l2LntqEV9bf0lYsduaQ1BFdQ2vLN7MST060L+LjdRrvCMiTBnWgynDerBh536ue3Eplz25iDvOG0DnpDgK9h7kpjP7ER1pv1fbKksiIWbBhl3cN2c1G4tKeeqqBq+INsYT/bsk8c4tp/E//1rJw+/nHNqeHB/DNaf29i4wE1D28yCEzM8p5Krnl1BZU8vz12Zx/onWlGWCS4d20Tw+fThZvZIPbfvbx7kcrKz2MCoTSJZEQkRFdQ33/HsV/Tsn8t6tp3HWwC5eh2RMg0SEx38wgkuG9+C5a7LYdaCCR+blNP1CE5IsiYSIVxZtJn9PGfdcdCLxMdYKaYJb1w5x/PmKYZw9qAvXjO3Fi1/ksTzfxt5qiyyJhID95VXMnJ/Laf1TGd/f5gkxoeW/Jg6kU0IsD76zxuYmaYMsiYSA5xZsYu/BKn51/kCvQzHmmCXGRnHHeQNYmrfX5iZpgyyJBLnSimqe+3wTE0/syknpHbwOxxi/XJGVwdkDO/Pbd9das1YbY0kkyM1Zvo39FdXceHofr0Mxxm8REcIfLz+Zzklx/OL1ZRyosKu12gpLIkHutaX5DOiSyIieHb0OxZjj0jE+hkemDmXz7oPc+FI2ldU2eGNbYEkkiJWUV7E8fx/fG9rdJpoybcKp/VJ59LKhLNq4mwffsZmy2wK7VjSIrdlWAsAQ6wsxbcglw9NZs62EZz7fRP8uSVw1ppfXIZnj0KyaiIj0FZFYd3mCiNwiIta+EmCr3SRyYvf2HkdiTMv674kDOfOENH7z9io+XLPT63DMcWhuc9ZbQI2I9AOeAzKBVwMWlQFg9bZi0pJi6ZwU53UoxrSoqMgInrxqJAO7JvH/Zq+2KXdDWHOTSK07U+ElwGOq+gugWxOvMcdpzbYSq4WYNis2KpJ7vjeYrfvKeG7BJq/DMX5qbhKpEpHpwDXAXHdbdGBCMgC1tcrGXaWcYEO9mzbs1L6pnH9iF2bOz6WwpNzrcIwfmptErgPGAr9V1U0ikgn8vakXichEEckRkVwRubORclNFREWkxaduDFW7SiuorK4lPbmd16EYE1D/c8EgqmpqbZDGENWsJKKqa1T1FlV9TUSSgSRVfaix14hIJDATmAQMBqaLyOAGyiUBtwBLjjn6NmzbPudXWfeOlkRM29arUwLXj8vkza8LWLW12OtwzDFq7tVZn4hIexFJAZYDL4jIn5p42WggV1U3qmolMAuY0kC5B4CHAavL+ti2rwywJGLCw01n9SMlPob759ogjaGmuc1ZHVS1BLgUeEFVRwLnNPGaHkC+z3qBu+0QERkOZKjqXMwRthc7ObVbB7syy7R97eOiuf28AXy5aQ/vr9rhdTjmGDQ3iUSJSDfgcg53rDeloVusD/3EEJEI4M/AHU0eSGSGiGSLSHZRUVEz3z607T5QQVSE0KGdXb9gwsMVWRkM7JrE795ba5f8hpDmJpH7gXnAt6q6VET6ABuaeE0BkOGzng5s81lPAoYAn4hIHjAGmN1Q57qqPq2qWaqalZaW1syQQ9vuA5V0Soyx4U5M2IiKjOA3Fw0mf08ZLyzM8zoc00zN7Vh/Q1WHqupP3fWNqvr9Jl62FOgvIpkiEgNMA2b7HLNYVVNVtbeq9gYWA5NVNduvM2ljdpdW0Ckh1uswjGlV4/qlcs4g55Lfov0VXodjmqG5HesDROQjEVnlrg8VkV839hr35sSbcWowa4HXVXW1iNwvIpOPN/C2bpdbEzEm3Nx94SDKqmp48tNvvQ7FNENzm7OeAe4CqgBUdQVOzaJRqvquqg5Q1b6q+lt32z2qOruBshOsFnLY7tIKUhOtJmLCT2ZqApNP7s5rX25h38FKr8MxTWhuEolX1S/rbbNZZQJoX2mVdaqbsPXjM/pwsLKG561vJOg1N4nsEpG+uFdXichUYHvAogpzNbXK/opqSyImbA3s2p5JQ7ry/IJN7C212kgwa24SuQl4ChgoIluB24CfBiyqMHeg3KnktbckYsLYL84dQGllNc8vtMEZg1lzr87aqKrnAGnAQFUdr6p5AY0sjJWUVwHQPs7mDDPha0CXJM4b3IWXvshjv/uZMMGn0W8pEbn9KNsBUNWmhj4xfjiURKwmYsLczyb0Y97qnby6ZAs/PqOv1+GYBjRVE0lq5JEY2NDCV0mZ05yVZDURE+ZOzujI+H6pzJyfS/6eg16HYxrQ6LeUqt4HICLjVHWh7z4RGRfIwMLZ4eYsq4kY89tLhnDR3xbwk79/xVs/PZW46EivQzI+mtux/rdmbjMtoKTMSSJ2dZYxzlDxj10xjNXbSvjN26tslN8g01SfyFjgVCCtXv9Ie8B+DgRISd3VWVYTMQaAswd14edn9eNvH+cyrGdHfnhKL69DMq6mGt1jcPo+onD6QeqUAFMDFVS4q6uJJFqfiDGH3HbOAJYXFHPPv1fTvWM7zjyhs9chGZruE/kU+FREXlTVza0UU9grKa8iKTaKyAgbwdeYOpERwswfDOeKpxbzs79/zWszxjAso6PXYYW9RvtEROQxd/FxEZld/9EK8YWl/eXVdnmvMQ1IiovmxetHkZoUw1XPLmHTrlKvQwp7TbWXvOI+PxroQMxhJWVVdnmvMUfROSmOV380homPfcZt/1zGI1OHMqBLUtMvNAHRaE1EVb9ynz9t6NE6IYafkvIq61Q3phEZKfE8PPVkcnfuZ8rjC1mev8/rkMJWc+cTGSci/xGR9SKyUUQ2icjGQAcXrkrKqmnfzmoixjTmwqHdmP/LCaQkxPCDZxbz0dqdXocUlpp7n8hzwJ+A8cAoIMt9NgFgNRFjmqdz+zje/OlY+qQl8tO/f80LCzfZfSStrLlJpFhV31PVQlXdXfcIaGRhrKSsyjrWjWmmbh3a8coNozmtfyr3zVnDfXPWUFNriaS1NDeJzBeRR0RkrIiMqHsENLIwVevOJWIj+BrTfB3jY3jyqpGc2rcTL36RxxPzc70OKWw095vqFPd5pPssOBNUndXYi0RkIvAXnLvbn1XVh+rt/wnOXCU1wAFghqquaWZMbVJpZTWqNoKvMccqOjKCf/zoFG775zIe+2gDY/p2YlTvFK/DavOaOxT8XPdZgSJggao2OlOMiEQCM4FzgQJgqYjMrpckXlXVJ93yk3H6XSYe81m0IXVDntglvsYcOxHhwYuHsDx/Hz9+xRmwMTM1weuw2rTmDgWf6D6ScDrV3xORaU28djSQ605oVQnMAqb4FlDVEp/VBNzpd8NZ3ZAn1rFujH+S4qJ54brRAFz7wpfsOlDhcURtW7OGgq9PRFKAD3ESw9H0APJ91gs43Czme6ybgNtxxulqtHksHBxKItacZYzfMlMTeO6aLKY/s5gbXlzKazPGEB9jtftAaG7H+hFUdQ9Ov0hjGtr/nZqGqs5U1b7AfwO/bvBAIjNEJFtEsouKio453lBiI/ga0zKG90xm5g9GsHJrMTf942uqa2q9DqlN8iuJiMhZwN4mihUAGT7r6cC2RsrPAi5uaIeqPq2qWaqalZaWdkyxhprDNRH71WTM8Tp7UBcevPgk5ucU8Zt/r/Y6nDapqY71lXy39pCCkwyubuLYS4H+IpIJbAWmAT+od/z+qrrBXb0Q2ECYs1kNjWlZPzilJ1v2HOTJT79l0pCunD6gbf8QbW1N/dy9qN66ArtVtcmhM1W1WkRuBubhXOL7vKquFpH7gWxVnQ3cLCLnAFU4NZtrjvkM2pj9dnWWMS3uF+f2Z97qHdw7ezUf/OJ0oiL9aoQxDWiqY/245hBR1XeBd+ttu8dn+dbjOX5bVFJWRXxMpP0nN6YFxUZF8j8XDOLGl7P597JtfH9kutchtRn2TRVkbNwsYwLjnEGd6ZOWwKylW7wOpU2xJBJkbARfYwJDRLhsZAZL8/ayseiA1+G0GZZEgozVRIwJnEtH9ADg3ZXbPY6k7bAkEmRKym0EX2MCpUv7OIb0aM9nG3Z5HUqbYUkkyJSU2Qi+xgTSaf3T+HrzXg5UVHsdSptgSSTI7LeaiDEBdVr/VKprlcXf2pRILcGSSBBRVUrKq+0eEWMCaETPZKIjha+2NDXohmkOSyJB5GBlDTW1ah3rxgRQXHQkA7u2Z3n+Pq9DaRMsiQSRQ0OeWHOWMQF1ckYHVhYUU2vT6B43SyJBpKTMRvA1pjUMTe/I/opqNu5qcgQn0wRLIkHkcE3E+kSMCaST0zsCsKLAmrSOlyWRIGKzGhrTOvqkJRAdKeTs3O91KCHPkkgQqRvB1/pEjAms6MgI+qYlsmGnDX9yvCyJBJG65iy7xNeYwOvfJYn1VhM5bpZEgkhdc5YlEWMC74QuiRTsLaPU7lw/LpZEgkhJeTVx0RHERkV6HYoxbV7/LkkAbCi0Jq3jYUkkiJSU2Qi+xrSWAW4SybUkclwsiQQRG8HXmNaTntyOyAhh8267V+R4WBIJIjaCrzGtJzoygh4d25G3+6DXoYS0gCYREZkoIjkikisidzaor2NXAAAUIElEQVSw/3YRWSMiK0TkIxHpFch4gp2N4GtM6+rVKd5qIscpYElERCKBmcAkYDAwXUQG1yv2DZClqkOBN4GHAxVPKCgpr7Y+EWNaUe9OCWzaVYqqjaHlr0DWREYDuaq6UVUrgVnAFN8CqjpfVevqkouB9ADGE/RKyqrs8l5jWlGvTvHsL69m38Eqr0MJWYFMIj2AfJ/1Anfb0dwAvNfQDhGZISLZIpJdVFTUgiEGD2cuEWvOMqY19e6UAECeNWn5LZBJRBrY1mCdUUSuBLKARxrar6pPq2qWqmalpaW1YIjBo7yqlqoam0vEmNbUOzUegM3Wue63QLadFAAZPuvpwLb6hUTkHOBu4AxVrQhgPEHNRvA1pvWlJ8cjYknkeASyJrIU6C8imSISA0wDZvsWEJHhwFPAZFUtDGAsQc9G8DWm9cVFR9KtfZxdoXUcApZEVLUauBmYB6wFXlfV1SJyv4hMdos9AiQCb4jIMhGZfZTDtXklNoKvMZ7o1SmBTZZE/BbQthNVfRd4t962e3yWzwnk+4eSQ81ZdnWWMa2qZ0o8H60L64aQ42J3rAeJwyP4Wk3EmNaUkdKOXQcqKKus8TqUkGRJJEgcbs6ymogxrSkjxblCq2Cvda77w5JIkLCOdWO8kZ7sJJF8SyJ+sSQSJErKq4iJiiAu2uYSMaY1ZSS3AyB/T5nHkYQmSyJBwhnB12ohxrS2tKRYYqMiyN9jNRF/WBIJEs4IvtYfYkxrExHSk9tZc5afLIkECRvB1xjvZKTEU7DXmrP8YUkkSNgIvsZ4JyM53pqz/GRJJEjYCL7GeCcjpR0l5dUUl9mQ8MfKkkiQsI51Y7yTUXeZr9VGjpklkSBRYh3rxnjGbjj0nyWRIFBeVUNlda3VRIzxyOGaiHWuHytLIkHg8FwilkSM8UL7dlEkxUbZZb5+sCQSBPbXjZtlV2cZ4wkRId0u8/WLJZEgYONmGeO9jOR21rHuB0siQcBG8DXGe3U3HKqq16GEFEsiQWDfwUoAOlifiDGeyUhuR1lVDbsOVHodSkixJBIE9h10mrOS42M8jsSY8FV3ma91rh+bgCYREZkoIjkikisidzaw/3QR+VpEqkVkaiBjCWZ7Sq0mYozXDiUR6xc5JgFLIiISCcwEJgGDgekiMrhesS3AtcCrgYojFOw7WEmHdtFERVrF0BivpLvzitgVWscmkD25o4FcVd0IICKzgCnAmroCqprn7qsNYBxBb+/BKpLjrRZijJfiY6JITYyxu9aPUSB/+vYA8n3WC9xtpp69ByvpaP0hxniuR3K83bV+jAKZRKSBbX5dOyciM0QkW0Syi4qKjjOs4LP3YCUpCZZEjPFahk1OdcwCmUQKgAyf9XRgmz8HUtWnVTVLVbPS0tJaJLhgsre0io7WnGWM5zJS4tm2r4yaWrtXpLkCmUSWAv1FJFNEYoBpwOwAvl/I2nuwkhRrzjLGcxnJ8VTVKDtKyr0OJWQELImoajVwMzAPWAu8rqqrReR+EZkMICKjRKQAuAx4SkRWByqeYFVeVcPByhqSrTnLGM/1dC/z3bLbmrSaK6DjbKjqu8C79bbd47O8FKeZK2zZjYbGBI8+aQkAfFt0gLF9O3kcTWiwGxM8ttcd8sQu8TXGe906xBEfE8m3RQe8DiVkWBLx2F73bnVrzjLGeyJC37REvi0q9TqUkGFJxGNFByoASE20JGJMMOiblsC3hVYTaS5LIh7bUexcBdK1QzuPIzHGAPRNS2TrvjIOVlZ7HUpIsCTisR0l5STGRpEYa3OJGBMM+nVOBODbQmvSag5LIh7bUVxOl/axXodhjHEN7NYegLXbSzyOJDRYEvHY9uJyunaI8zoMY4yrV0o8ibFRrNpW7HUoIcGSiMfy9xw8dIOTMcZ7ERHC4G7tWb3NaiLNYUnEQ/vLq9hdWknPlASvQzHG+BjcvT1rtpXYGFrNYEnEQ5vdoRV6d7KaiDHBZEiPDpRV1bDRbjpskiURD9XdFZuZZjURY4LJyF7JACzZtMfjSIKfJREPrduxn+hIoU9qotehGGN89O4UT7cOcSz6drfXoQQ9SyIeWru9hL5picRE2Z/BmGAiIozt04nFG3dTa/0ijbJvL4/U1irfbNnH0PQOXodijGnA2L6d2F1ayRq7X6RRlkQ8sqHwAMVlVYzqneJ1KMaYBpw9qAtREcKcFX5NyBo2LIl4ZGme02E3OtOSiDHBKCUhhvH9U5m7fLs1aTXCkohHlubtIS0p1m40NCaIXTK8B1v3lfHxukKvQwlalkQ8UFldyyc5RZzWLxUR8TocY8xRXHBSN3p3iufRD3LsxsOjCGgSEZGJIpIjIrkicmcD+2NF5J/u/iUi0juQ8QSLj9bupLisigtO6uZ1KMaYRkRHRvDL809g3Y79/PGDHK/DCUoBSyIiEgnMBCYBg4HpIjK4XrEbgL2q2g/4M/CHQMUTLErKq/jTf9bTMyWeMwd29jocY0wTLhranWmjMnjik2956L11VFTXeB1SUAnkJBajgVxV3QggIrOAKcAanzJTgHvd5TeBx0VEVNXzeqOqUl2r1NQefhaBCBEi3Gc4cl2EBpunqmpq+WbLPuYs38a/l23lYGUNL1w3isgIa8oyJhQ8cPEQRODJT7/l7W+2MnFIV07JTKFv50Q6JcTQMT4mbD/PgUwiPYB8n/UC4JSjlVHVahEpBjoBu1o6mNeX5vP05xvdpFBLTc13k0TNofVajqf5M+JQshEQJ4moQkxUBJOGdOX6cZmcnNGx5U7OGBNQ0ZER/P7SoVxwUjdeWJjHq19u4cUv8o4oExMZQVSkEBUhRLvLESIIzo9L50cmCM4PT3H34XxNNMut5wxg8sndW/bkjlMgk0hD/y71v5qbUwYRmQHMAOjZs6dfwSQnxHBC1ySiIoTICDn07CxHfGfb4fUIoiKc/wAAqlCrSq37DM6Ng3XrilOLqStTlzz6d07kzIGdbQZDY0LYaf3TOK1/GuVVNWzYeYBNu0vZW1rJntJKKqprqa6ppbpWqaqpPfTj0flOcL4X6r4fFNzvB/3uF14jkuOjA3NixyGQ32gFQIbPejpQ/66dujIFIhIFdAC+M+KZqj4NPA2QlZXlVx3h3MFdOHdwF39eaowxR4iLjuSk9A6cZCNOBPTqrKVAfxHJFJEYYBowu16Z2cA17vJU4ONg6A8xxhjTPAGribh9HDcD84BI4HlVXS0i9wPZqjobeA54RURycWog0wIVjzHGmJYX0AZ6VX0XeLfetnt8lsuBywIZgzHGmMCxO9aNMcb4zZKIMcYYv1kSMcYY4zdLIsYYY/xmScQYY4zfJNRuyxCRImCz13E0IZUADN3SyuwcgkOon0Ooxw9t5xwSVDWtpQ8cckkkFIhItqpmeR3H8bBzCA6hfg6hHj/YOTTFmrOMMcb4zZKIMcYYv1kSCYynvQ6gBdg5BIdQP4dQjx/sHBplfSLGGGP8ZjURY4wxfrMkcgxEJFJEvhGRue56pogsEZENIvJPd8h7RCTWXc919/f2OcZd7vYcETm/lePPE5GVIrJMRLLdbSki8h/3HP4jIsnudhGRv7qxrhCRET7HucYtv0FErjna+wXoHDqKyJsisk5E1orI2FA6BxE5wf33r3uUiMhtoXQO7nv/QkRWi8gqEXlNROJC6fMgIre6sa8WkdvcbUH/NxCR50WkUERW+WxrsbhFZKT7HZHrvrbpSRdV1R7NfAC3A68Cc93114Fp7vKTwE/d5Z8BT7rL04B/usuDgeVALJAJfAtEtmL8eUBqvW0PA3e6y3cCf3CXLwDew5l9cgywxN2eAmx0n5Pd5eRWPIeXgB+5yzFAx1A7B59ziQR2AL1C6RxwprXeBLTz+RxcGyqfB2AIsAqIxxnJ/EOgfyj8DYDTgRHAKp9tLRY38CUw1n3Ne8CkJmNq7Q9OqD5wZmb8CDgLmOv+I+8Cotz9Y4F57vI8YKy7HOWWE+Au4C6fYx4q10rnkMd3k0gO0M1d7gbkuMtPAdPrlwOmA0/5bD+iXIDjb+9+eUmonkO9uM8DFobaOeAkkXz3SyjK/TycHyqfB5zpJ571Wf8N8KtQ+RsAvTkyibRI3O6+dT7bjyh3tIc1ZzXfYzj/0Wrd9U7APlWtdtcLcD5ccPhDhru/2C1/aHsDr2kNCnwgIl+JM289QBdV3e7Guh3o7G4/WqxenkMfoAh4QZxmxWdFJIHQOgdf04DX3OWQOQdV3Qo8CmwBtuP8//6K0Pk8rAJOF5FOIhKP84s9gxD6G9TTUnH3cJfrb2+UJZFmEJGLgEJV/cp3cwNFtYl9jb2mNYxT1RHAJOAmETm9kbLBeA5ROFX5/1XV4UApTvX9aILxHABw+wsmA280VbSBbZ6eg9vmPgWnCao7kIDzf+po8QTVOajqWuAPwH+A93Ga1KobeUlQxX8MjjVuv87HkkjzjAMmi0geMAunSesxoKOI1M0OmQ5sc5cLcH7Z4O7vgDP976HtDbwm4FR1m/tcCPwLGA3sFJFubqzdgEK3+NFi9fIcCoACVV3irr+Jk1RC6RzqTAK+VtWd7nooncM5wCZVLVLVKuD/gFMJoc+Dqj6nqiNU9XQ3lg2E1t/AV0vFXeAu19/eKEsizaCqd6lquqr2xmmC+FhVfwjMB6a6xa4B/u0uz3bXcfd/rE4j42xgmnu1SiZOZ96XrXEOIpIgIkl1yzjt8avqxVr/HK52r/AYAxS7VeV5wHkikuz+Ij3P3RZwqroDyBeRE9xNZwNrQukcfEzncFNWXayhcg5bgDEiEu9evVP3dwilz0Nn97kncCnO3yKU/ga+WiRud99+ERnj/l2v9jnW0QW6E6itPYAJHL46qw/Of/pcnGaJWHd7nLue6+7v4/P6u3GuQsmhGVc+tGDcfXCq7cuB1cDd7vZOOBcMbHCfU9ztAsx0Y10JZPkc63r33HKB61r5338YkA2sAN7Gubok1M4hHtgNdPDZFmrncB+wDueHyCs4V1iF0ufhc5zEtxw4O1T+BjjJbjtQhVNzuKEl4way3L/pt8Dj1LuIpaGH3bFujDHGb9acZYwxxm+WRIwxxvjNkogxxhi/WRIxxhjjN0sixhhj/GZJxASUiPy5bpRUd32eiDzrs/5HEbm9hd/zQEsezz3mMBG5wGf9XhH5ZTNeJyLysYi099l2iYioiAwMQJy9ReQHLX1cn+PfLCLXBer4JvRYEjGB9gXO3cyISASQCpzos/9UYKEHcR2rYThjLB2rC4Dlqlris206sADnxtWW1htoMIn43E1+PJ4HbmmB45g2wpKICbSFuEkEJ3mswrkrNllEYoFBwDcikigiH4nI1+58BlMAROQPIvKzuoO5NYA73OX/EpGl7lwJ9zX05g2VcX+trxWRZ8SZT+IDEWnn7hvlll0kIo+IM+dEDHA/cIU4c4Bc4R5+sIh8IiIbReRoX6w/xOeuXxFJxBlG5wZ8koiITHCPVTdXyj/cu4YRkQvcbQvEmeOhbj6bM+TwvCTfuCMSPASc5m77hYhcKyJviMgcnME3xee8Vtadi/v+n4rI6yKyXkQeEpEfisiXbrm+AKp6EMgTkdFN/eFNmGjNu1ztEZ4PnCHoewI/Bn4CPIDzC30c8JlbJgpo7y6n4txJK8Bw4FOfY61xj3UezrzRgvNjaC5wulvmgPvcYBmcX+vVwDC33OvAle7yKuBUd/kh3CG3cebLeNwnjntxalmxbry7gegGzn0zkOSzfiXwnLv8BTDCXZ6AM7ptuhvrImA8zt3e+UCmW+41Do+YMAdnUE2ARPffcELdfp+4Czh8F/P3cQYejAS64Axh0s193T53ORbYCtznvuZW4DGfY94N3OH1/yt7BMfDaiKmNdTVRk7F+XJc5LP+hVtGgN+JyAqcSYJ64Axx/Q3QWUS6i8jJwF5V3YKTIM4DvgG+BgbijL3kq7Eym1R1mbv8FdBbRDrifOHXxfRqE+f1jqpWqOounEHvujRQJkVV9/usT8cZxBP3ebrPvi9VtUBVa4FlOMluILBRVTe5ZXzH21oI/MmtBXXUw8Ow1/cfVd3jLo8HXlPVGnUGf/wUGOXuW6qq21W1AmfYiw/c7SvdWOoU4ozeawwt0UZqTFPq+kVOwvmlnw/cAZTgtLGD0+yTBoxU1SpxRkyOc/e9iTNwX1cOfwEL8HtVfaqR922wjDjTs1b4bKoB2tHwUNiNqX+Mhj5P1SISoaq1ItIJZwToISKiOLUBFZFfNXK8o8akqg+JyDs4tbrFInLOUYqW+iw3do6+71/rs17LkecWB5Q1chwTRqwmYlrDQuAiYI/7C3gPzrS2Y3FqJeAMD17oJpAzcaaMrTMLp/9gKk5CAWck0uvdPgZEpIe4I7P6aE6ZQ1R1L+4opu4m347v/UDSsZy0KwdnYELc+F9W1V6q2ltVM3BmahzfyOvXAX3k8Lzkdf0xiEhfVV2pqn/AGZRyYDPi/AynbydSRNJwmveOdeTcATg/BoyxJGJaxUqcfoPF9bYVu01BAP8AskQkG6dWsq6uoKquxvli3KqHZ3D7AKe5aZGIrMRJLkd8eTanTANuAJ4WkUU4v9qL3e3zcTrSfTvWm+MdnP4GcJqu/lVv/1sc5Woq9xzKcOYof19EFgA7fWK6ze0gX45TM3gPZ3TjahFZLiK/aOCQ/3LLLAc+Bn6lzhD7x2IcTpOjMTaKrzG+RCRRVQ+4y3fizF1963EcrxtO7ePc443JvVprJrBBVf/s7/GOh4gMB25X1au8eH8TfKwmYsyRLnRrG6uA04AHj+dgbs3pGfG52dAPN4rIMpx5YDoAjfUDBVoq8BsP398EGauJGGOM8ZvVRIwxxvjNkogxxhi/WRIxxhjjN0sixhhj/GZJxBhjjN8siRhjjPHb/weNdH+1sCT17AAAAABJRU5ErkJggg==\n", 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"text/plain": [ "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -756,8 +778,10 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 86, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", @@ -779,8 +803,10 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, + "execution_count": 87, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -788,7 +814,7 @@ "text": [ "synphot count rates:\n", "\t HAT-P-11: 2.5 million e-/s\n", - "\t TRAPPIST-1 6.5 thousand e-/s\n" + "\t TRAPPIST-1 1.279131256718357e+16 thousand e-/s\n" ] } ], @@ -809,7 +835,7 @@ "metadata": {}, "source": [ "\n", - "## 10. Comparing simluated counts to observed counts" + "## 11. Comparing simulated counts to observed counts" ] }, { @@ -821,8 +847,10 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 88, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "hatp11_r_apo = 34000000\n", @@ -833,8 +861,10 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, + "execution_count": 89, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -867,29 +897,29 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": {}, + "execution_count": 90, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 44, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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WEhKk11+Xjh+XrPV9f/1133jRA+ou1KFDh5wnCrds2VKtW7d2to0xSkxMVHx8vG666SYd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\nLhc4ZgUn6SxJfyXpBUlHJHVJWlyj6yJJz6R9/kV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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -920,7 +950,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] } @@ -928,7 +960,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -942,7 +974,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.5.2" } }, "nbformat": 4, From f015cb937fff8e8935e26ec94e187734cc6d21c8 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Thu, 13 Jun 2019 16:56:03 -0400 Subject: [PATCH 14/26] push changes --- observing_examples.ipynb | 232 ++++++++++++++++----------------------- 1 file changed, 95 insertions(+), 137 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index d195c6b..21adefc 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -61,10 +61,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -113,18 +111,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query finished.\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -156,6 +145,13 @@ "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query finished.\n" + ] } ], "source": [ @@ -188,10 +184,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "T_eff = round(T_eff, -2) # round to nearest 100 K\n", @@ -217,19 +211,19 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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J0yFQjPR5YjHenTgQXWOUkIniQSh3IjV8fqrCqhsZ42RQbpK74RvTISSUWLXOKs/lf/4E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\nTkSuQmCC\n", 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Ccaup/9lsOoSwmP7wTSS/e2Uj2jbKxID2jUyHQuR6QZOIiLyGEJ1lVHWsIxFRVJrVzTAdQliuyG1lOoSwTJi9En/+WW9c3LOF6VCIXC1UTeR/4xYFVVtKSuyrIoM6NMJHfpMWSjWrPKkp3ll94OY56/D+Z4fwyPiepkMhcq1Qf9G7VHV5sJ+4RWhQ8TFvNKr/9qLOYR13QZcmQZ+7NsAyspf3caLWUNVIEHfdl3upcC/n2iIKIVQS+Xfphoi8EodYXOe6572xBkWzehlhffQ2rRf8llcXv/mv/nJlH/ykT8tqRlaZenSAOBMJUWChkojv51J7pwNxow17vbOkfKwb1kf3aF7tW1eB+CcR/9qRmzsIMJEQVRYqiWiQbfK4fu0aGju3+r2V/naNt8atZucVQL1anSJyQKgkcpaIHBWRYwB62ttHReSYiByNV4AUnkaZNas8plaNVADA+LMrt3WckZ4a85gCSYTP33ZTFmP/19+bDoPIFYImEVVNVdW6qlpHVdPs7dLHYc34JyIjRWSbiBSJSF6A5+8QkU9F5BMRWSoirlnhaO+R70yHEJHsxplVHnPHhZ1w+/BOuLx3xbaOe8d0xeic5mGdp7p3m0LlkAfHdg85OaObDMp/B4+8sdV0GETGOfYXKyKpAJ4GMApAN1iLWXXzO2wdgFxV7QlgPoBHnYonUv9alXijlmulp+G24R2R5vdBfd257St0Eb5zRPlU79PGdY/oHK0bnhHy+ZIQVZGrB2VHdC7T/m/ZDnS+N6kmtCaqxMmvff0AFKnqTlX9EcBc+E2XoqrvqmrpV/6VAFwzEm3m8h2mQzDmlgvKp3rPTA9n3bJyqW5uGXfAiVMlbHCnpOZkEmkJYK/P4332vmCuRZKtUxLIHSOcW/CpKqvvvgDLfzuswr7Gday2lpb1Q9cwwtWkjjdG1kcqO68A37t8yQAiJziZRAJ9JQ14L0NEJgHIBfBYkOcni0ihiBQWFyf2BMLXnJMd0fHZjWpFdZ7LA4wBaVI3A20bVWxbGdopC89ek4vbLqjeQlSlnBh74hZdp76B6Ys+NR0GUVw5mUT2AWjt87gVgP3+B4nIcAD3ABirqgGHiKvqbFXNVdXcrKwsR4L1O5/j5wh+7siOn3nV2WEd99ClORUeP35Fr7DPcX6XplVOqzLnl/3x4Niq208S/W7X3z7YxdtblFScTCJrAHQUkXYikg5gAoCFvgeISG8As2AlkIMOxhKRlz/eZzqEsHVpFt7SuDH78A5SzqAOjcNqGE+ELr7hyM4rwNEfTpoOg8hxjiURVT0F4GYASwBsATBPVTeLyDQRKZ0B+DEAtQG8LCLrRWRhkOLi6q75n5gOIaauHtg24NgQclbPB97EHfPWmw6DyFGRdb2JkKouBrDYb99Un+3hTp6fLA+Oy6n6IJeqk5GGYz94d/3zBWu/wIK1X2DX70c7Mo0MkWneGNmVxNbdNwIDPbg40p1BepmVNtyH29vr5RsGokNW1QMp3a7dlMVYsvm/psMgijkmET+m72Nn1Kj4X9IgMx3/vK5/VGXNuCwHy34zLAZRAe3sEfG9W9cP6/iLzwq8mFN6mnV9r944CM//ol+V5Qgk7hO3nXOmM0n7+hc+ZqM7JRwmET8vrPjc6PlrplWewyo1ygWnGtZKD2s6lHCc3bYB3v3NMEwaEHpmmqsGZgMAGtdOD3lck7oZGNLJ6mk3/dIc9GhZL+ixPUM854Q+bRo4Wn52XgGWf5bYXdUpeTCJ+HlsyTbTIZTxX+MjUhkxnlSxXePMKu/rXzu4HXbnj0GdjBphl3vVgLZ47ZbBAIC01Irl10xLQf5PeuLfN50TecBRuqh7MzSuXRMD2pfPdjxzUp+YnuPqZ1ezVkIJgUnExVr4tBvMifCW1rWD22FYJ+fH1MRa56Z1KqzUmN04Exk1UtErzNto1fVR3vnIaVkPhfcOxwvXlv+bN3FoDfvsvAI8tXS7I2UTxQOTiIs98dPyAYGDzmwc0WsnDWjryd5AIoKbzjsz7uf9x8/7Ynf+mAqJu0ZqCnq3sZKXkwNQ//DWZ8jOK8A333NcCXkPk4iL1Tsj/FtC0Vhw4yC8efsQR88RreFdm8b1fMM6B15/PsVOxKpwfJr6sx58E3mvJNYYJUp8jo4T8ZpkW7HO6Qbk6ph11dk4ebrEdBhlA/RLFGhePwOfH3Z2nZm5a/Zi7pq9mHFZDq7s75rldYiCYk3Ex6HjP5oOAQCwO38MduePqVYZ9R2uxTgtNUWQUaNix4B//Dz+S+mW3hFUVbw0eWDcznvPq5uQnVeAQ8cDTidH5BpMIj4ef8s9PbOqY919I9AgM3QXWy8a1rkJNtx/YbXLqV8r/ARb2q6kAJrVi/809rkPvc1eXORqTCI+Xly9t+qDYqxGaviN34vsbrBVScQEUipQO1EsEkswpUN0Qq3IGA/ZeQVMJuRKbBMxrHuLeli/9+uwjs2pYtDd+3edh+MnvDvPVLSc7IAgKG9Yd4PSRFLd251EscKaiGH3X+K/7Hz0Wjesha7Nw5sa3ssK7x2Ol2+w2ifG9bKmV1l0y2DcPrwT1txTcU7P3flj0Mwe43FGjcgHX5a3iVQjYAdk5xVg4uyVpsMgYhIxrXQuKQpf49o10Te7IWZO6oOHL+sBwKql3Ta8I7Lq1MSOh0cDKE8AK6acj9V3X4A/TbDG3XRuGv5MAKNymgEA2torSP5qWIdYXUa1rdh5GNl5Bbj62dWmQ6Ekxk8w29ffme+Z1bFJbdMhOKpORmzvno7MaY7MmpXLTE0RzLmuP9777XkArMbxJnUzcGH3ZtidP6Zs3fhwTBrQFpsfvAitG1pJ5Hcju6BoxihMHtI+NhcRA8s/K0Z2XgHO/99lpkOhJMQ2EVuvaW8ZOW9dnzmmOlVzriy3853OxGmhRvhHMo5fRColqrTUFGSmu+9PZ+ehb9lmQnHHmohhpd9wASDuc547LNzeZPHmxelgIlXamyvZBtBS/DGJUNKJZQr5H5cvO9xuymJk5xXg+x9Pmw6FEhSTiIuUTvZHziqtiDzx07OiLkPtamNzAwMQo9F16hvIzivAvML4j4WixOa+G7tJLNTCTBQ72fYSvY0ya+Kx8T2rt3CXCD6+dzh2FH+LK2atiFGEzrlr/ie4a741ySPbTSgWmEQAnHLBRH9A4nX37dq8Lq7s3wZ7jnyH97cfMh1OmVvOPxO929QvW1kxGrltG9q/G6BR7ZpoVNvq8dWiXgb2f/NDTOIM5bzOWXh3W/VWRyxthN/84EUBe7kRhSOxPrWidJdLpt8OZ+GlBTcOQqem3ugKnJoimHFZj7Jv/m6RlpoSdOr3cA3u2Bgb7r+wQiLaOn0kltndip122/BOMSur+/1LkJ1XgCtmur8mRe7DJAJgwdovTIcAILxeQ33aNEBLn4WTyBz/6VYyaqQiPS0Fc37ZH1NGdXH03E6s9Lh695GyXl3bDxyLefmUmJhEDLrkrBamQ4iLofa39bNaJUfHgUEdGuP6oR1QcGvlLs6Na1eeHDO7Ua1K+0wb8cR77CZMYWESMejeMV1NhxAXw7s1xdbpI3FWnNZJd4vuLepVarxu37jyrcjTLv+QLu0m/It/rDEdCrkQk4gHmViDvLr8F5hKJq/8ahAW3TIYW6ePxON2t+K7RpaP3i8J0q/D6eV4I/XO1oNltZPVu46YDodcwl3vUgpLbnZD0yFQBM5u2wA5Lesho0YqWjWohe0zRuFXQztg5qQ+AIA5v+wf8HWZNVMxaUCbssepKeENk3SivcTfFbNWlCWU37++xfHzkXsxiRjk8rsY5JAaqSkQEYzMaY7d+WPQtlEm/m4v/Vs60zAANMxMR4pPZ4tPwlx8K9xkEyuzlu8sSyg3vPBxXM9N5jmaRERkpIhsE5EiEckL8PwQEVkrIqdEZLyTsRC52Xmdm2B3/hiM69USq++5AACw8ObB6NKsfH0Y/7EctdLLbxH6Lvkb7yTi643N/y1LKDn3L2GjfBJwLImISCqApwGMAtANwEQR8V+BaQ+AawDMcSqORPX0z/pgvr0wEyWWJnUysDt/DDJrpuGs1tYsBuec2ajSceN95u1ad9+Isu00g0nE1/ETp8oa5bPzClB87ITpkMgBTg5T7QegSFV3AoCIzAUwDsCnpQeo6m77OXcMGfeQMT2bmw6B4qB7i3p4cmJvnN/FGhy58+HRZb257r+kO55f8Tnm3zCwwhijcztmYc4vB7huTfa+M94u2545qQ9G5vA9nAicTCItAfjO9rYPQOAWRIM+P/yt6RCIQhrrM54oJUWQYs9DnJoiFboQb50+EgvWfoGf9W9TqQx/1w9tj+KjJ7BgnZmBtjf8c22Fx9seGomaacnbg8/LnGwTCVSnjuoGqYhMFpFCESksLq7efEH+hj62LKblEZmSUSO1QgK5emDbsu1+7Rri8t4tAQBv3j4EU0Z1xSPje5Y9/9TE3vELNIDO975Rdtvr6XeL2JbiIU7WRPYBaO3zuBWA/dEUpKqzAcwGgNzc3IR5d9WqyW9e5Jz7Lu6Gm84/E03qlE9X//hPy3t/1UhNwbr7RiA1VbD9wPEKr90ybSTOsBvu431b7LEl2/DYkm1lj68f0h5TRifHwFwvcrImsgZARxFpJyLpACYAWOjg+Tzl79f0LVsaN//yHph3PRvJKbbSUlMqJJBAGmSmo25GDfi2xe/OH1OWQABgQt/y74I7Hx5d4fVX2jWfxbeei4U3nxODqCub9V55F+LsvAJMe+1T1lRcxLGaiKqeEpGbASwBkArgWVXdLCLTABSq6kIR6QvgVQANAFwiIg+qanenYjLhnTuH4vw/LK+0v3XD8kkUJ/Sr+h42kZO6NKuL9lmZyL+8Z6XnHhjbHXPX7MWYHhUbwm+7oCNuH9EJMy7rEa8wAQDPfrgLz364q8I+35oTxZejiwio6mIAi/32TfXZXgPrNpcRK3cedvwc7bNq46FLc3Dvvzc5fi6iaJ2Rnop37hwW8LmMGqn4dNpFlRq+bx9ReTr6R3/SM+DSCjcO64C/LNsRk1gD6Tr1jQqPZ111Ni7q3syx81G5pF6J5j/ro2qiIUo6tdKtj4qSEus2Uk7LugGPKwlym2n82a0cTSL+rg8wcn713RegSV1vLGfsJUmdRJZtO+hIudMvzcF9rHlQAkpJEcy7fmDQhdGCLYnjhlU7+z28tNK+DfdfWGldGIpMUieRk6edaZy7akDbMJKIO0YVE0WqX7vgE4Be2rslfvfKxrLHS349BCdPl6CGy2YkLnXWg29W2vfGr8+tMN0MhZbUSeTQ8dhNw9AwMx1Hvv0x4HNhLFhIlBBqpqWi4NbBGPPkB1h651B0yLJqLLH8W3PayD++X2nfuF4t8PgVvYzOS+ZWSZtEig4er/qgCPRsVQ/LtsV2ICSRFwVajMst83lF6z/r9wdsQ505qQ8u6t4srKWtE1XSJpFYfzNit3Wi4BL1G7z/9C2lHh3fE+P7tEJKgl63r6RMIqdLFBNmrzQaQ4v67CVCySMzPbk+au6a/wnuml+5q3ODWjWw9M5haJiZbiAqZyTX/6ztt/M3xLzMSCoiaSlS1mWSKBkkwzfycHz13Un0mf5W2WP/235e5M4uEw5bsDb2M5dyGgYiSkZJmUScsuiWwVjy6yGV9tfJYD90ovd+e57pEMgBTCIxlNOyHjo3q1P2eES3pgCAi3tw8R2iNo1qmQ6BHJB0SeTYDycdKbe23/rXu/PH4Jn/lwvAuh98+/DyeYb6tw8+WIsokRXNGGU6BIqxpEsiK3Y4M+li20aZYR/7076ctZeSU1pqCj6ddpHpMCiGki6JrN51xHQInPCEklqt9DSsvW+E6TAoRpIuifzjo91Gzntup8ZGzkvkRg0z07E7fwzyRnUxHQpVU9INVjhVYqYrbp82DYycl8jNbhjaATcM7YBvT5zCv1Z9jocXbw37te0bZ2L6pTno3aY+3t9+KOD07+S8pEsiROQ+mTXTMHlIB0we0iGq11/UvRl2PDwaS7ccwG1z1+P7k6djHCEFwyQSI0k8/xqRK6SmCC7s3gxbpo8EYA0A3nvkexQVH8P6vd/g4NEfsGrXEew69G1c4kkRoPTGR3paCto2rIU6GWnIrJmGAe0b4Zqv4ZtUAAAM2ElEQVRB2XGJw2lMIgaUTo9NRM4REbRpVAttGtXC+V2axqTM9z4rRq829VGXA4jLJF3Deqy1rH9GxK/p1oIL3hB50ZBOWUwgfphEqiG3bQP8rH9kYz7uHs3eKESUOJIqiew5/F1MyxvdozmGdsoCAAzvWnV1eXf+mKgbDomI3Cip2kSu/2fsuwDmtKy8ihsRUbJIqprIli+PxrS8Hq3qxbQ8IiKvSaqaSCytnzoC9WslzupkRETRSKqaSCwxgRARJVESOXT8hOkQiIgSTtIkkdyH3jYdAhFRwnE0iYjISBHZJiJFIpIX4PmaIvKS/fwqEcl2Mh4iIootx5KIiKQCeBrAKADdAEwUkW5+h10L4CtVPRPAEwAecSoeIiKKPSdrIv0AFKnqTlX9EcBcAOP8jhkH4Dl7ez6AC0RiP5Xh429ui0k5t17QEV2b1w1rYCERUTJwsotvSwB7fR7vA9A/2DGqekpEvgHQCMChqgqfOHslisNoLD95ugSfRzFS/bzOWXh3W3GFfXeM6IQ7RnQK8goiouTjZBIJVKPwXxEqnGMgIpMBTAaANm2suaraZWWiYWbV3WwVGnESmXf9QPRr17C8DFU4UEEiIvI8J5PIPgCtfR63ArA/yDH7RCQNQD0AlRZBV9XZAGYDQG5urgLAw5f1cCDkwJhAiIgCc7JNZA2AjiLSTkTSAUwAsNDvmIUArra3xwN4R1XNrF9LREQRc6wmYrdx3AxgCYBUAM+q6mYRmQagUFUXAvgbgBdEpAhWDWSCU/EQEVHsOTp3lqouBrDYb99Un+0fAPyPkzEQEZFzkmbEOhERxR6TCBERRY1JhIiIosYkQkREUWMSISKiqInXhmWIyDEAsZkMy50aI4xpXzwska8vka8N4PV5XWdVrRPrQr24PO42Vc01HYRTRKSQ1+dNiXxtAK/P60Sk0IlyeTuLiIiixiRCRERR82ISmW06AIfx+rwrka8N4PV5nSPX57mGdSIicg8v1kSIiMglPJVERGSkiGwTkSIRyTMdTzAi8qyIHBSRTT77GorIWyKy3f7dwN4vIvKkfU2fiEgfn9dcbR+/XUSu9tl/tohstF/zpBNLCldxfa1F5F0R2SIim0XktkS6RhHJEJHVIrLBvr4H7f3tRGSVHetL9hIHEJGa9uMi+/lsn7Km2Pu3ichFPvuNvpdFJFVE1onIogS8tt32e2d9aY+kRHlv2uevLyLzRWSr/Tc40Oj1qaonfmBNJ78DQHsA6QA2AOhmOq4gsQ4B0AfAJp99jwLIs7fzADxib48G8DqsVR4HAFhl728IYKf9u4G93cB+bjWAgfZrXgcwKs7X1xxAH3u7DoDPAHRLlGu0z1nb3q4BYJUd9zwAE+z9MwH8yt6+EcBMe3sCgJfs7W72+7QmgHb2+zfVDe9lAHcAmANgkf04ka5tN4DGfvsS4r1pn/85ANfZ2+kA6pu8vrhdeAz+4QYCWOLzeAqAKabjChFvNiomkW0AmtvbzWGNdwGAWQAm+h8HYCKAWT77Z9n7mgPY6rO/wnGGrvU/AEYk4jUCqAVgLYD+sAaipfm/H2GtmTPQ3k6zjxP/92jpcabfy7BWGV0K4HwAi+xYE+La7HPuRuUkkhDvTQB1AeyC3Z7thuvz0u2slgD2+jzeZ+/ziqaq+iUA2L+b2PuDXVeo/fsC7DfCvr3RG9a39YS5Rvt2z3oABwG8Bevb9deqeipATGXXYT//DYBGiPy64+WPAO4CUGI/boTEuTYAUABvisjHIjLZ3pco7832AIoB/N2+HflXEcmEwevzUhIJdF8uEbqWBbuuSPfHnYjUBvAKgF+r6tFQhwbY5+prVNXTqtoL1rf2fgC6hojJM9cnIhcDOKiqH/vuDhGPZ67Nxzmq2gfAKAA3iciQEMd67frSYN0q/z9V7Q3gW1i3r4Jx/Pq8lET2AWjt87gVgP2GYonGARFpDgD274P2/mDXFWp/qwD740pEasBKIP9S1QX27oS6RgBQ1a8BLIN1P7m+iJROFeQbU9l12M/Xg7Xcc6TXHQ/nABgrIrsBzIV1S+uPSIxrAwCo6n7790EAr8L6EpAo7819APap6ir78XxYScXc9cXzXmU17wWmwWr8aYfyBrvupuMKEW82KraJPIaKDV+P2ttjULHha7W9vyGse58N7J9dABraz62xjy1t+Bod52sTAM8D+KPf/oS4RgBZAOrb22cAeB/AxQBeRsXG5xvt7ZtQsfF5nr3dHRUbn3fCanh2xXsZwDCUN6wnxLUByARQx2f7IwAjE+W9aZ//fViTKQLAA/a1Gbu+uL5pY/CPNxpWT6AdAO4xHU+IOF8E8CWAk7Ay+7Ww7iMvBbDd/l36HyYAnravaSOAXJ9yfgGgyP75uc/+XACb7Nf8GX6NbHG4vsGwqrifAFhv/4xOlGsE0BPAOvv6NgGYau9vD6vnShGsD92a9v4M+3GR/Xx7n7Lusa9hG3x6ubjhvYyKSSQhrs2+jg32z+bS8yfKe9M+fy8Ahfb789+wkoCx6+OIdSIiipqX2kSIiMhlmESIiChqTCJERBQ1JhEiIooakwgREUWNSYQcJSJPiMivfR4vEZG/+jz+g4jcEeNzHo9leXaZvURktM/jB0TkN2G8TkTkHRGp67PvMhFREeniQJzZIvKzWJfrU/7NIvJzp8on72ESIad9BGAQAIhICoDGsAaqlRoE4EMDcUWqF6zxD5EaDWCDVpwWZiKAD2AN3ou1bAABk4jPiPTqeBbArTEohxIEkwg57UPYSQRW8tgE4JiINBCRmrDmpFonIrVFZKmIrLXXMhgHACLyiIjcWFqYXQO4097+rYissddJeDDQyQMdY39b3yIiz4i1XsibInKG/Vxf+9gVIvKYiGwSa22NaQB+KtYaFT+1i+8mIstEZKeIBPtgvRLWLMel8dSGNfXItfBJIiIyzC6rdJ2If5Wu4yAio+19H9jrO5SuATLUjme9PRlfHQD5AM61990uIteIyMsi8hqsSQnF57o2ll6Lff7lIjJPRD4TkXwRuVKsdVU2ikgHAFDV7wDsFpF+Vf3HU5IwMVKWP8n1A2tq7jYArgdwA4DpsL6hnwPgPfuYNAB17e3GsEbRCqwZgpf7lPWpXdaFsNaMFlhfhhYBGGIfc9z+HfAYWN/WTwHoZR83D8Ake3sTgEH2dj7sqWsAXAPgzz5xPACrllXTjvcwgBoBrv1z2NNw2I8nAfibvf0RytdlGQZrhtxWdqwrYM0MkAFrttV29nEvonyU+WuwJhsEgNr2v+Gw0ud94t6H8hHMP4E1K3EqgKYA9sCa/nsYgK/t7ZoAvgDwoP2a2+AzxQ2skep3mn5f8ccdP6yJUDyU1kYGwfpwXOHz+CP7GAHwsIh8AuBtWNNPN1XVdQCaiEgLETkLwFequgdWgrgQ1vQkawF0AdDR77yhjtmlquvt7Y8BZItIfVgf+KUxzaniugpU9YSqHoI14V3TAMc0VNVjPo8nwpr4EPbviT7PrVbVfapaAmsqmWw75p2quss+5kWf4z8E8LhdC6qv5VO5+3tLVY/Y24MBvKjWLMUHACwH0Nd+bo2qfqmqJ2BNefGmvX+jHUupgwBaBDkXJZlY3CMlqkppu0gPWN/09wK4E8BRWPfYAeu2TxaAs1X1pD3LbIb93HwA4wE0Q/kHsAD4varOCnHegMeItQbKCZ9dp2FNtBjpMqf+ZQT6ezolIimqWiIijWDNmpsjIgqrNqAicleI8oLGpKr5IlIAq1a3UkSGBzn0W5/tUNfoe/4Sn8clqHhtGQC+D1EOJRHWRCgePoQ1C+4R+xvwEVhLeg6EVSsBrCnGD9oJ5DwAbX1ePxdW+8F4WAkFsFbS+4XdxgARaSkiTVBROMeUUdWvYLXXDLB3+TZ8H4O1FHCktsGaFBB2/M+raltVzVbV1rBmTx0c4vVbAbSX8rXNS9tjICIdVHWjqj4Ca0K+LmHE+R6stp1UEcmCdXtvdYTX1AnWlwEiJhGKi42w2g1W+u37xr4VBAD/ApArIoWwaiVbSw9U1c2wPhi/0PLV296EdbtphYhshJVcKnx4hnNMANcCmC0iK2B9a//G3v8urIZ034b1cBTAam8ArFtXr/o9/wqC9Kayr+F7WOucvyEiHwA44BPTr+0G8g2wagavw5rZ9ZSIbBCR2wMU+ap9zAYA7wC4S1X/G8H1AFZb1tsRvoYSFGfxJfIhIrVV9bi9nQdr3erbqlFec1i1jxHVjcnurfU0gO2q+kS05VWHiPQGcIeqXmXi/OQ+rIkQVTTGrm1sAnAugIeqU5hdc3pGfAYbRuGXYq33vhnWbb9Q7UBOawzgPoPnJ5dhTYSIiKLGmggREUWNSYSIiKLGJEJERFFjEiEioqgxiRARUdSYRIiIKGr/HzDToNZyJ83+AAAAAElFTkSuQmCC\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -267,10 +261,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", @@ -295,20 +287,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -347,10 +340,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# Set up parameters for the observing night in the format that skyclac_cli would use\n", @@ -379,10 +370,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { @@ -390,7 +379,7 @@ "[2018]" ] }, - "execution_count": 14, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -428,19 +417,19 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -472,19 +461,19 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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UQoJxy3m9WF1eoblGJGzCmUQ2AAUh6538skOY2bnAL4BLnHMHDrcj59wk51yx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\nf8MbCC8XeMEvM+A3zrmHjvK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+ "image/png": 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -512,19 +501,19 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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A3P1xkjcVSgRiGCcCSZWWgohIXCp9skx195+XbcvXOjNSH0nvrHRXZ0HSuK7qLJG4VBpE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\nnzGztWa2xsy+a2b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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -545,10 +534,8 @@ }, { "cell_type": "code", - "execution_count": 75, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "band = 'rprime' # only want to know about the r' band\n", @@ -573,10 +560,8 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "countrate = observation.countrate(area=aperture_area) / gain" @@ -591,17 +576,15 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HAT-P-11 counts in the r-band, 10 s exposure:\n", - "44043008 cts\n" + "44209346 cts\n" ] } ], @@ -631,10 +614,8 @@ }, { "cell_type": "code", - "execution_count": 82, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", @@ -659,20 +640,21 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 20, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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ITqzcKH3HWSfhtRns5unNGzM3edxfeLwUdRPjISIBnU9VUVKm1do5fvfKHADA9YPTgoqT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\n3f4gAuAN875WAUh3O89NADa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RuNfL4EzyalAnPWdj0pC7PL9JsJGGxiSKqIlHVc9S1TdU9aj531X1TW/CMsluyMntgg4hECMnryzd/mTBpqPe35J/gEc+Xlw6w8G7OeuZsswm3zDppcI/S0VkARU/U3MQWAn8WVXnexGYSW41aqTnqLZIvsvbxd8+X8EXS7dw1vEtOOv4FvzhPWfgga1satJJpP6Qy6Ic1xP4F9AnngGZ5NWlZX027bZZjkJ1vvvI2JofPj+Vri3rA1Bc7qmBMbPW8/9OaY8x6SBS4lkX6ZkaYKWI2AgzU+rzO88OOoSEU37S0IIKFo4bnWOJx6SPSPd4JovIr0WkzHwzIlJLRM4TkdeBVd6GZyJ57JMlzLcZC5LSx999z56DR5LQ5vwDLN2UH2BExvgnUuIZCBQBb4vIRhFZLCKrgBXAUOAZVf2XDzGaCoz6ahWDR04NOgwAMsvd08mwuVvD2pTvdEW+NzuPn/1rVun+vJ37Gfi3rz255sZd+3lu0goid2AY458Ku9rchztfAF5wZwpoDuxXVfsTOwGUzIacKOrWyijz+owuzQOKJHnMXB1+WsL9h4qonVmjSgM0DhUWUyuz7N+Tv/rPHOat38XFPVpxQqsGVYrVmHiKaQ5hVT2sqt9b0kkcf/50ael2v8c+DzASx6lZTcu8zrBRbVVSVKz84P7PeGDsIgBmr90RtqVyuKiYQvcB1aJiZfqq7XyzYhvH3/sps9eWTWgHDhcBzoCG9Tv2lb4O59uV2xk6anrpuY3xQno+5ZcCVm/bW7q9OT/41o8tixQfJfd9Rues55wTWjDs9RxuPrMjf7i4G7/6zxw+X7K5tGzjejWZd/9FPP9FLs98vpze7Z05fGeu3snJxzVl36FCvlq+jaWbCgB4euJyJi52jl/12KWlLarDRcXUECGjhnDH6Llszj/I1j0Had2orp9VN2nEEk+Smr12Z9AhGA/0enAC4HSZlSSMl79ezctfrz6q7K59hwHI3boHgBWbnfJLN+UzLXcbN7+Rw95DR1o3JUkHoJM7zHv5I5dw/L2f0qphHabffX7prOKhjazv8nbx/e4DHFMrkwFdrQvVVF/UxCMi3VV1cbl956jqFM+iMknIblzH25Pjl0UtM3HxZj6a7yxzVZJkPpy3kQ/nlV/6KryBz34FOIMexuSsp6SH9MvlW1nyfT4jLu/BD58/MoDlpjOyeODyHpWphjFHiaXFM0ZE3gT+AtRxf2fjLIltAlBcbF/yxnHzG5HWYoxu1dYjXbZ/fO/I8g13/W8BAJOWlJ3O57Wpa9i59xA79x1mYM9W9OnQmG6tGlYrBpN+Ykk8/YAngGlAA+A/wBleBmUi63T3J9EL+czuB6SmDbuOmqKRD9zW1JfLtwLw8OAe/OS04+w+n4lZLInnMLAfqIvT4lmtqjbkxZRxz6AfBB2CCch9Hy6iZcM69GjTkOb1a1OnZkb0g0xaiyXxzAI+BE4BmgEvicgQVR3iaWQmqdiXTXr7+ZuzS7cXjLiIBnVqBhiNSXSxPMczTFXvd5/l2aSqg3ESkTHGHOXEERNslgQTUSwtni3l52sDvvQiGBPdku9tPi+T+Dre5dyHnH7X+bRqVCfgaEyiiaXFMw742P09CWdi0E+9DMqEt3rbXqav2h50GMbE7LQ/T2LXvkNBh2ESTNQWj6qeGPpaRPoCP/csIlOhc/86JegQwrrjgq5Bh2ASWO+HJgLw8a8H0LNto4CjMYkgprnaQqnqHJyBBsYA8OvzLPGY6C77+zcA5O3cx96D4dclMukhlpkL7gx5WQPoC2z1LCKTdGxCUBOrH70wlTnrdtGjTUPG3X5m0OGYgMTS4mkQ8lMb517PYC+DMsakpjnrnAnuF23M58KnvyRr+DjWhEx4a9JDLPd4HvQjEGNMelmxxZncdMqyLdzYvGPA0Rg/VZh4ROQjIsz8qKo/9CQiY0xaWbQxny35B3hhykr+OPAE6tWySfNTXaT/w3/1LQpjqum1G0/hppClpE3yeHd2Hu/OzgOg6TG1uP18G6yS6iIlntWqus63SExE9iR4ZOd2axl0CCYOnp64nK+Wb+W9X54edCjGQ5EGF3xQsiEi//UhFhPB1aOmBx2CMb7IWbvTlv5IcZEST+gY2U5VObmIDBSRZSKSKyLDw7xfW0RGu+/PEJEsd38zEZksIntE5Plyx5wsIgvcY56TNJmLfebqHUGHENZF3Y8NOgSTgi7+21dkDR9nz/ukqEiJRyvYjomIZAAjgUuA7sBQEelertgwYKeqdgGewVn3B+AAcB/w+zCn/gdwC9DV/RlY2dhM/LRpbOvwmPgrGfE29OXpbC04aHMUpphIiaeXiOSLSAFwkrudLyIFIhLLp+BUIFdVV6nqIeAdjn7+ZzDwurv9HnC+iIiq7lXVb3ASUCkRaQ00VNVv1bnp8QZwRQyxGI8kwr2nlg1qBx2C8ch3ebs55dHPueTZr9mc73wdLNywm8NFtiRYMqsw8ahqhqo2VNUGqprpbpe8jmWt27bA+pDXee6+sGVUtRDYjbPmT6Rz5kU5p/FR8GkHGtSx4bfpoN9jk1jyfT6X/f0bHh23JOhwTDVUeq62Sgh376X891QsZapUXkRuEZEcEcnZutVm+PFKAjR4TBq55NmvAfguz5kB4dy/TuF3Y+YHGZKpAi//VMwD2oe8bgdsrKBMnohkAo2ASHfR89zzRDonAKo6ChgFkJ2dbV+PHtEEaPOkyfgSE2LF5j0cLCxi9ba9rN62ly4t67Pk+3zyDxzmqat60ay+db8mMi9bPLOAriLSUURqAdcAY8uVGQvc4G4PAb7QCDcNVPV7oEBETnNHs12PrYYaqERo8XRsfkzQIRifFRws5IR7Pyt9/cRnSxk7fyNTlm3l3g8WBhiZiYVnice9Z3MbMB5YAoxR1UUi8pCIlEy38yrQTERygTuB0iHXIrIGeBq4UUTyQkbE/RJ4BcgFVmKL0gXquGb1KnxvQJfmvsTwzNW9fbmOSQ57DhZy+9tzyRo+LuhQTAU8vSurqp8An5Tbd3/I9gHgqgqOzapgfw7QM35Rmuq4sk+7Ct9r2dCf7o76tW1wgTni6xXbSrc/mLuBvh2a0KBOJk2OqRVgVCaU/Ys11WJf+iaR3TF6Xun2mscHUVhUTGaGl3cYTCzs/0ASOHC4KOgQKlS3VkaF77Wzh0tNApm4eDNd7vmU6au2M3TUdAb+7augQ0pb9udqEuh232fRCyUg69owieTmN3IAmLZyO9+u2g7Arn2HaFzPPqd+sxaPSRm/Pq8Lb93cL+gwTIJ7btKK0u3eD00s3d6973AQ4aQlSzzGM34MtX7g8iPT//3uohM4vXNzPrn9TO8vbFLGjr2HmLNuJ70emsBpj03iL58tZcXmAp6ZuLx0mh4TX9bVZlJO9zaxzOhkjKPvw0daPZvclVBfmLISgGcnrWBgj1a8+NOTgwovJVmLxxhjIvhs0SamrdzG7n2HOVhYxPod+4IOKelZi8dU2bPX2IObJj1c+/IMAM7s2pyvV2xjxt3nU6dmBo3q1qSwqJi/jF/GLWd1orlN1RMTa/EksH2HChNi2YGKDO4d/MTg3VpZt5rxT8nDqf0em0SvBycAMHnZVkZ9tYoHPlwUZGhJxRJPgtqx9xDd7x/PyMm5QYdSZRf18H510v6dI62iYYz3Ct21gYpsue6YWeJJQEXFyuCR3wDw1wnLA46m6to1qXgeN68tfujiwK5t0sfKrXsocnslPlu0iaGjpnP2k5MDjirxWeJJQHPW7WT9jv1Bh5HU6tWy25fGe+c/9SV7DhSWvv521XbWbt/HyMm5LN9cEGBkic0STwLafyhxp8gxxpQ1/H8Ljtr35PhlXPTMV3S++xNytxQweta60vcKi4qZu26nnyEmHEs8CajIwwEFocsYZNZI7gXUhg3oGPH9X5/XxadIjAmvqFi54Omv+NN/F7D3YCFbCg7Q5Z5PufKFaSzcsDvo8AJjiScB3fTaLM/O3a9j09LtLi3re3adRHB5rzZBh2BMGc9+fmS6ni0F6TsrgiUek7Q6tbCVR03yWLdjH/+bs6H0dXExjJycy469h0r37TtUyK59h8IdnlIs8Zikde2pHSK+n9wdiSbVXPLs1+wPWeJk6sptPDl+GX0fnsg1o75l9tqddL9/fJmJS2ev3ZGSw7Qt8aQZSaGvY5HUqYtJP69NXVO6PX3VDm5/e27p6937DjNz9Q5+/I9vGTk5F1VlWu62hH6gvDIs8aSZ0O/q6nxxp/r9IWP8tmHXkUcorh71LWPnO91yS77P57lJuVz7ygz+PWNdRYcnFXvYwVRJp+aJf3/FGkQmWS3dVMDSTc5zQJ8u3MSnCzcBpMwEpdbiSTPhvoyrMqravtSN8d/eg4XRCyUBSzxprCR3tGpYpwrHJkPmSYYYjYndzhQZ8WaJx1SJEuxNzpdiWJjLWmUm1RQXBx1BfFjiMVVKIbec1TnucVTGxT1aBXp9Y4JQbKPaTHI60gwoaRFUpWHQsI6NSzHGb+t3psbkwZZ40ky8up8a1q0ZU7nWjSp//yherKfNpJol3+cHHUJcWOJJMIs2+jdxYFWTUM+2DTk2xgEJP2htK4QaY8qyxJNgnpu0InqhOCkZmVbZXuNTsppGL+Q6sW2jSp49ukEnto6pXM0M+3gbk4jsX2aaiUf3U9eWDWIue/v5XeNwxbJi7eZr3zS4FVCNMRWzxJNgkmHQytBT28dcNiPJ1/wxlde+ad2gQzAJztPEIyIDRWSZiOSKyPAw79cWkdHu+zNEJCvkvbvc/ctE5OKQ/WtEZIGIzBORHC/jD8KExZt9v2Zlk13Qk3Ne3/+4QK9vIkuVZ02MdzwbEysiGcBI4EIgD5glImNVdXFIsWHATlXtIiLXAE8AV4tId+AaoAfQBvhcRI5X1ZI5xc9V1W1exZ7Kyk4SGlwc1WEDFoxJbl62eE4FclV1laoeAt4BBpcrMxh43d1+DzhfnD+nBwPvqOpBVV0N5LrnS2mh67J7JTmmujHGpDIvE09bYH3I6zx3X9gyqloI7AaaRTlWgQkiMltEbqno4iJyi4jkiEjO1q1bq1URvzw/OdfX6zVwHwK95ESbBcDET6o8XW+84+Xj5+H+tC7/iayoTKRjz1DVjSLSEpgoIktV9aujCquOAkYBZGdnJ8W/hD0H/J159phamcy7/0Ia1KlZZlGqRLZgxEVBh2CisLzjnYt7HBt0CHHhZYsnDwgd/tQO2FhRGRHJBBoBOyIdq6olv7cA75NCXXA79x32/Brl7+s0rlcrqUaeNagT21BqU9ZXfziXx648MegwTDWlSlL3MvHMArqKSEcRqYUzWGBsuTJjgRvc7SHAF+qs7ToWuMYd9dYR6ArMFJFjRKQBgIgcA1wELPSwDr7JGj4u6BBMCuvQrB7X9uvgy7Wsq807qfLf1rOuNlUtFJHbgPFABvBPVV0kIg8BOao6FngVeFNEcnFaOte4xy4SkTHAYqAQuFVVi0TkWOB9dzhvJvCWqn7mVR3M0R69smfQIZhKOrZhbV+vlxpfjYmpqDg1/ut6OsWwqn4CfFJu3/0h2weAqyo49lHg0XL7VgG94h9p+qhup1rHZpVf8vqRK3py7wfVb5iedXyLap/DeC/cH+X/uukUbnxtFvVrZ7InRVbRDMKU5ckxUCoam7kgzVT34c+q/L11eudm1bqmqZ7jj419iqP4XK8+ACe1O3qePltOo3pSpKfNEk8iOFhYFL2QoZXPXUap4Ib+xzHyur6+XrNzCyfxDDm5Xem+ku/LLiFJ8NUbsv0MyyQQSzwBevjjxdz7wQJOuDe1b1PVj9NfuSN+2CMu50kn/Ts3o6EPIwF7tj0ym0SkZdEF+PewfmQ1q8cZXZrzv1+dTrdW/rbITPCs3RugV79ZHXQIvmjZID6LwdWrZR/Xyurdvokn521SryY79x2mT4fGzF23q8x7Jd1BFXXqDujanCl/OBeAvh2aMOYX/cnbsZ8aNeBfU9fwzqz13HdZd75duZ3Pl/g/d6Hxnv1LNpXSvoktNZAs1jw+yLNz92rfmCnLtnLuCS2Zu25XmXsPpZsi/HtYPzIzhP2HK+5OblinJt3bOK2yBy7vQZvGdbm+/3EMG9CR9Tv2ceZfJnsWz38AAAAU9UlEQVRWDxMMSzymUjo0Cybx/GXISYFcN5FMG34e/56+lhemrAw6lFIlrZpwN70Fp3UDkH/gMK0b1eGOCyKvz1S3VkaZNZxsTaXUZIknzSTrjNQNaqf3R7Vn24a0aVyX5vWrN8Di0hNbMW/dLjbuPlCt85R2p8X4eWpYpybf3nV+ta5p/H8myys2uCDNBDU79e8vOr5ax1/YPTXmqKqKE9s24h/XnQwcGaJ8+3ldwpaNNnT9hetOZtpd5zM9TkmgZHh+aIPndxcez5V92vKjvuXnBDbVdX3/rKBDiAtLPAFZtqkg6BB8ddt5VV8C+6qT25GZEZ+P6sODj4yM++yOM+NyTi/1at+YD289o7TLKTurKfPuv5A7LzohbPm3bj4tpvO2anT0gI9hAzrGHFe4+f1uO7cLvzi7M83q1+aZq3vbYBAPFKfIzAWWeAKyetveoEOotLo1MwK57pNXVX2yimNqlY35p/2zWP7IJaz+86UJvTbRjadn8eJP+vLhrWdQo9yXfON6tSIee8cFXWnbOPry06d3blb636d+7Uzuu6w7me61+neK3HL6849O5KenHcdV2c6zOoN7t+H3F5/A8Eu6Rb1uZbUOkyTTVVGKPEFqiScg2/Yc9PT8f/5R+JmIq3OP56Nfn1H1g6uoZYPq9Wn/+//6lW6Pv+MsAGpl1kBEwv7VH6RamTX497B+rHl8ECN+2IOBPVtHLP/ST53ut0vLrad0xwXHM3X4eVGv99bNpzH97rJdbiX3kF7/2amseuzS0v3/N6Ajvds3Lp154NiGdXj4ip60bFCHZY8M5OdndYpewSr6+o/nenbuZFMzTi3/oFlbOEWdFuUv1qoIYkmCD2+rXrLr0+HIcywnlHtQsVHdmvx9aB9+/fbcal0jXhY9eHGlvlgu7tGqdMj0B3M3VGlJ8Pq1M7m+/3Glswy8+4v+TF+1nVqZThw/O6Mjyzbnc+9l3QHYsGs/y8t1E9fO9LYlHK9u1lRw7an+zDDuNUs8Kapj88pP5hnNsQ2r10Lo0rI+uVv2VOqY1o2idxlFE+l5lqC6D8urbNIp74o+VbuRLyI8NPjIjOPtm9YrM4T5/su7lynftnHdmLrxjDdqZqZGEk6NWiSh/APeL/oWTriJG/3Sr2PTwK5dkXO7tQw6BH5+VieOSfPh4tE8aNMlpRRLPAF56ctVvl/zgcu7M7h3cENc7xn0g8CuXZGgV1997xf9+dPA+N+QTzU3nJ4VdAgmjizxBEQ9HJ1ydXb7sPuPq8asA+2aVL97JdLw2jPdJ9xD1fepFTD6ltiGIMfb/AcuIjur6VGj1oypiF//JrxmiScg+Qe8Wwzrl+d0rvQxp0bpBnvm6t5VDScm9w7qftS+T3/jz3M2/TwYiBHNmscH0aiu/4M1kpkX9y1NMCzxpJDLTmrNhN+eRVYF/0AjNbKiPbcRrxvK1/YLPyrnhFYN+NPAbpyS1YR/3XQKTw45KWXn6YplqLM52uTfnxN0CCZOLPH4LG/nPqblbvPk3Lec1SniapOREs/ZJ0ReVrpNnBLPY1eeyN+H9uHMrs15u9xT9r88pzPv/uJ0zjmhJVdV0F3oFT+eFXn+2j4se2SgjQozac8Sj88GPDGZa1+Z4cm5T2rXuMrH+nmX4fJebXhzWL+EGuXWvmm9qCP+amYIfTtU7b9xg9qZXHZSG8+feUl1ix+6OOgQquTq7PZ89Ydzee3GU8rMvj02zHNqTeqF74IteWA4FaTGnSrjqZIn/uOtRg1nvZZ4rVBaXWNvG0DW8HEVvr/i0Ut55OPFzCm38Fk0t5/flTsvrN4kqcZRr1YmIy7vzoiPFgcdSlSv3XQKJx/XhAa1M0snU+3QrB7ndmtZ5vPw96F9OL1zM05+5HMALjmxNW/NWHfU+bq2rO9P4D5IjH/xJnCZNSpu/JZ/4j+eBoQZzRakacPP4/ynvqxw4bJfnNOZV2JcOfYnp3XgkSvCT11kqu7GMzpSo4Zw/4eLgg6lVPZxTTi1Y1M25R9g2ICOdG/dsDTZRHN5rzYAzLz7fIpUaVinJpk1hCEnt6NuzQz2HSqibq0MOrWwxGMC1KhuTXbvL/sA6sy7o09zH2kAd7Ku0xNvbRrXZcnDA8nbuY8BTzgrXx7XrB5PDnEmKm0aMkHnyGv7ktW8Hh2bH4MgrN2xl26tKj9tjam86/tn8aO+7ej5wPhKHffUVb0Y0LU5/R6bFJc4zuvWkocG96BdHFbmbRkyM0jobBKpyBKPj+Lx7M6axwfxp/e+Y3TO+jL7W8YwnU3J9W87twvPT84t816z+ke+UPt0aMzcdbuqPY1LMmvXpF7YqXZq1JAKp+CxpOOv+rUzWfLQQL5YuoVb35oTtfyjV/bkx+6cdGseH8SFT3/JikpO4dS2cV0e//GJdG3ZIOEmmU0mlnh8ctf/vqO4OD7nanxMbM9/XNT9WCYs3nzU/nO7teD5ybl0Dukzbt2oLpN/fw7tm9SlWOFQUbFN42ISXt1aGQw6qTWDThpEUbGy/3AReTv3MWnJFuas3cmwMztSt2ZGmcliS0y882wA9h0qZOe+w3yxZDOtGtWlQZ1MaohwUrtG1MqogQgxd5uZ2Ng3i0/enrk+eqEYDT2lA29NX0fBwcgPof71//Xiq+Vbue0tZ/blkvbWycc15a2b+3FKVtlRZaEP6NVKkckITfrIqCHUr51Jt1YNK9X6rFcrk3q1MvlpiqzumQzs2yXOiouVLQXVW88+mqzmx7DgwYt55fpsAO677Oin/sFZ5/6yk9qEfe/0zs3TthvNGBMsa/HE0Zx1O/nRC9MA+Md1fbnkxNbkbilg3HebPLneBd2PjTjlvzHGJCJLPHFUknQAfvmf6Dc7KyvatDbGGJMMPO1rEZGBIrJMRHJFZHiY92uLyGj3/RkikhXy3l3u/mUicnGs5wzKvkPeTfpZIsKjNhGVdMllH3f0DVZjjPGbZy0eEckARgIXAnnALBEZq6qhjxwPA3aqahcRuQZ4ArhaRLoD1wA9gDbA5yJS8qhvtHP6ZtaaHew9WMiNr83y9Dqv3XQKN702C6nixDbWJWeMSSRedrWdCuSq6ioAEXkHGAyEJonBwAh3+z3geXHGLQ4G3lHVg8BqEcl1z0cM5/SUqlJUrHS551O/Lkn/Ts24rl8Hbj23i2/XNMYYr3iZeNoCoWOI84B+FZVR1UIR2Q00c/dPL3dsydKZ0c4Z1uy1O/nTf7+jWBVVwv52fpzkcmSf87vAw/VzKvLhrWcwb/0u6tTM4NErbeoVY0xq8DLxhOsXKv/ofkVlKtof7i5H2OkAROQW4BaADh06cEztDE44tgEiUEPkqN81Sl+X7CvZ77x+beqaCitaXVN+fw4tGtSmxwPj6dj8GH530fGlw6B7ta/6jNPGGJOIvEw8eUDooirtgI0VlMkTkUygEbAjyrHRzgmAqo4CRgFkZ2drt1YNGXld36rVBHjg8h5VPjZWdh/GGJMOvBzVNgvoKiIdRaQWzmCBseXKjAVucLeHAF+oM6HYWOAad9RbR6ArMDPGcxpjjElgnrV43Hs2twHjgQzgn6q6SEQeAnJUdSzwKvCmO3hgB04iwS03BmfQQCFwq6oWAYQ7p1d1MMYYE38SjxmTE112drbm5OQEHYYxxiQVEZmtqtnxPq9N1mWMMcZXlniMMcb4yhKPMcYYX1niMcYY4ytLPMYYY3yVFqPaRKQAWBZ0HB5qDmwLOgiPpHLdwOqX7FK9fieoaoN4nzRd1uNZ5sWQwEQhIjmpWr9UrhtY/ZJdOtTPi/NaV5sxxhhfWeIxxhjjq3RJPKOCDsBjqVy/VK4bWP2SndWvCtJicIExxpjEkS4tHmOMMQkipROPiAwUkWUikisiw4OOJxIR+aeIbBGRhSH7morIRBFZ4f5u4u4XEXnOrdd3ItI35Jgb3PIrROSGkP0ni8gC95jn3CXG/apbexGZLCJLRGSRiPwmxepXR0Rmish8t34Puvs7isgMN9bR7lIeuMt9jHZjnSEiWSHnusvdv0xELg7ZH/hnWUQyRGSuiHzsvk6Z+onIGvfzM69kJFeqfD7d6zcWkfdEZKn777B/oPVzlnlOvR+cZRNWAp2AWsB8oHvQcUWI9yygL7AwZN9fgOHu9nDgCXf7UuBTnJVaTwNmuPubAqvc303c7SbuezOB/u4xnwKX+Fi31kBfd7sBsBzonkL1E6C+u10TmOHGPQa4xt3/IvBLd/tXwIvu9jXAaHe7u/s5rQ10dD+/GYnyWQbuBN4CPnZfp0z9gDVA83L7UuLz6V7/deD/3O1aQOMg6+frB9fn/9D9gfEhr+8C7go6rigxZ1E28SwDWrvbrXGeRwJ4CRhavhwwFHgpZP9L7r7WwNKQ/WXKBVDPD4ELU7F+QD1gDtAP58HCzPKfR5z1pPq725luOSn/GS0plwifZZzVficB5wEfu/GmUv3WcHTiSYnPJ9AQWI17Tz8R6pfKXW1tgfUhr/PcfcnkWFX9HsD93dLdX1HdIu3PC7Pfd263Sx+cVkHK1M/thpoHbAEm4vwFv0tVC8PEVFoP9/3dQDMqX28//Q34I1Dsvm5GatVPgQkiMltEbnH3pcrnsxOwFXjN7Sp9RUSOIcD6pXLiCdfHmCpD+CqqW2X3+0pE6gP/Be5Q1fxIRcPsS+j6qWqRqvbGaRmcCvwgQkxJVT8RuQzYoqqzQ3eHKZqU9XOdoap9gUuAW0XkrAhlk61+mTjd+P9Q1T7AXpyutYp4Xr9UTjx5QPuQ1+2AjQHFUlWbRaQ1gPt7i7u/orpF2t8uzH7fiEhNnKTzH1X9n7s7ZepXQlV3AVNw+sYbi0jJtFShMZXWw32/Ec7S75Wtt1/OAH4oImuAd3C62/5G6tQPVd3o/t4CvI/zx0OqfD7zgDxVneG+fg8nEQVXPz/7Uf38wcnyq3BuYpbcsOwRdFxRYs6i7D2eJyl78+8v7vYgyt78m+nub4rTl9vE/VkNNHXfm+WWLbn5d6mP9RLgDeBv5fanSv1aAI3d7brA18BlwLuUvfn+K3f7VsrefB/jbveg7M33VTg33hPmswycw5HBBSlRP+AYoEHI9jRgYKp8Pt3rf40z4SfACLdugdXP9w+uz/+xL8UZQbUSuCfoeKLE+jbwPXAY5y+IYTj94pOAFe7vkv/JAox067UAyA45z8+AXPfnppD92cBC95jnKXej0eO6DcBpen8HzHN/Lk2h+p0EzHXrtxC4393fCWe0Ty7Ol3Rtd38d93Wu+36nkHPd49ZhGSEjgxLls0zZxJMS9XPrMd/9WVRy/VT5fLrX7w3kuJ/RD3ASR2D1s5kLjDHG+CqV7/EYY4xJQJZ4jDHG+MoSjzHGGF9Z4jHGGOMrSzzGGGN8ZYnHJBQReUZE7gh5PV5EXgl5/ZSI3Bnna+6J5/ncc/YWkUtDXo8Qkd/HcJyIyBci0jBk35UioiLSzYM4s0Tk2nifN+T8t4nITV6d3yQnSzwm0UwDTgcQkRpAc5wHD0ucDkwNIK7K6o3zbEplXQrM17JTCg0FvsF5GDPesoCwiSdkVoLq+CdwexzOY1KIJR6TaKbiJh6chLMQKBCRJiJSG2cOtLkiUl9EJonIHHcdkMEAIvKEiPyq5GRuS+N37vYfRGSWu8bIg+EuHq6M2ypYIiIvi7PezgQRqeu+d4pb9lsReVJEFoqzLs1DwNXirO9ytXv67iIyRURWiUhFX8bX4czeXRJPfZwpa4YRknhE5Bz3XCVrrPynZA0UEbnU3feNuzZKyfo5Z7vxzHMni2wAPA6c6e77rYjcKCLvishHOJNmSki9FpTUxb3+lyIyRkSWi8jjInKdOOsSLRCRzgCqug9YIyKnRvsfb9JIUE9A24/9VPSDM0V9B+DnwC+Ah3FaAmcAX7llMoGG7nZznCepBWfm6y9DzrXYPddFOOvHC84fXB8DZ7ll9ri/w5bBaRUUAr3dcmOAn7jbC4HT3e3Hcac8Am4Eng+JYwROa662G+92oGaYuq/Fnb7Fff0T4FV3expH1jU6B2fW53ZurN/izBBRB2cG4Y5uubc5MtPARziTYQLUd/8bnlPyfkjceRx5iv3HOLNtZwDHAutwpsE/B9jlbtcGNgAPusf8hpDpkXBmK/hd0J8r+0mcH2vxmERU0uo5HecL9duQ19PcMgI8JiLfAZ/jTMN+rKrOBVqKSBsR6QXsVNV1OEnlIpypbeYA3YCu5a4bqcxqVZ3nbs8GskSkMU6SKInprSj1GqeqB1V1G86EjMeGKdNUVQtCXg/FmZgT9/fQkPdmqmqeqhbjTEOU5ca8SlVXu2XeDik/FXjabW011iNLGpQ3UVV3uNsDgLfVmX17M/AlcIr73ixV/V5VD+JMlTLB3b/AjaXEFqBNBdcyaSgefbjGxFvJfZ4TcVoU64HfAfk49wzA6ZJqAZysqofdmZPruO+9BwwBWnHkS1uAP6vqSxGuG7aMOGsIHQzZVYQzGWhlly8uf45w//4KRaSGqhaLSDOcmaB7iojitDpURP4Y4XwVxqSqj4vIOJzW43QRuaCContDtiPVMfT6xSGviylbtzrA/gjnMWnGWjwmEU3Fmd15h/uX9g6cpXr747R+wJlqf4ubdM4Fjgs5/h2c+yFDcJIQOKtd/sy9Z4KItBWRlpQVS5lSqroT5/7Tae6u0Jv/BTjLfFfWMpxJK3Hjf0NVj1PVLFVtjzMj8IAIxy8FOrnJEqDk/hIi0llVF6jqEzgTRnaLIc6vcO5VZYhIC5yux5mVrNPxOH9AGANY4jGJaQHOfZDp5fbtdrupAP4DZItIDk7rZ2lJQVVdhPNlukGPrLA4Aacr7FsRWYCTkMp84cZSJoxhwCgR+RandbDb3T8ZZzBB6OCCWIzDuX8CTrfa++Xe/y8VjEJz67Af+BXwmYh8A2wOiekOd5DAfJwWyKc4sxUXish8EfltmFO+75aZD3wB/FFVN1WiPuDcm/u8kseYFGazUxtTDSJSX1X3uNvDcdaw/001ztcap5VzYXVjcke5jQRWqOozVT1fdYhIH+BOVf1pENc3iclaPMZUzyC3VbMQOBN4pDonc1toL0vIA6RVcLOIzMNZW6YREOm+lteaA/cFeH2TgKzFY4wxxlfW4jHGGOMrSzzGGGN8ZYnHGGOMryzxGGOM8ZUlHmOMMb6yxGOMMcZX/x+TEagHg5ZugAAAAABJRU5ErkJggg==\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -683,7 +665,7 @@ "# Scale the flux to get the value at Earth\n", "stellar_radius = 0.117 * u.R_sun # radius of trappist-1 in solar units\n", "distance = 12.1 * u.pc # distance to trappist-1 in parsecs\n", - "photlam_trappist1 = photlam_trappist1 * (stellar_radius / distance).value ** 2 / np.pi\n", + "photlam_trappist1 = photlam_trappist1 * float(stellar_radius / distance) ** 2 / np.pi\n", "\n", "photlam_trappist1.plot(flux_unit='Jy', left=0, right=60000)" ] @@ -697,32 +679,25 @@ }, { "cell_type": "code", - "execution_count": 84, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "522921278184389214208 cts\n" + "306440 cts\n" ] } ], "source": [ "band = 'zprime'\n", - "# observation = Observation(photlam_trappist1, \n", - "# bp_atmos_qe[band],\n", - "# binset=bp_atmos_qe[band].waveset,\n", - "# force='taper')\n", + "\n", "observation = Observation(photlam_trappist1, \n", - " bp_atmos_qe[band],\n", - " binset=bp_atmos_qe[band].waveset,\n", - " force='taper')\n", + " bp_atmos_qe[band])\n", "\n", - "countrate = observation.countrate(area=aperture_area, waverange=waverange) / gain\n", + "countrate = observation.countrate(area=aperture_area) / gain\n", "counts_trappist1 = countrate * 10 * u.s\n", "\n", "print(\"TRAPPIST-1 counts in the z-band, 10 s exposure:\")\n", @@ -747,19 +722,19 @@ }, { "cell_type": "code", - "execution_count": 85, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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tPxGn32X8MZ9FGKkf8sRu8TXm2IkID1w4iKWFe/nJK86AjdnpSV6HFdaaOxR8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\nRCTX3ZYmIv8WkXXuz1R3u4j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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -778,10 +753,8 @@ }, { "cell_type": "code", - "execution_count": 86, - "metadata": { - "collapsed": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", @@ -803,10 +776,8 @@ }, { "cell_type": "code", - "execution_count": 87, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -814,7 +785,7 @@ "text": [ "synphot count rates:\n", "\t HAT-P-11: 2.5 million e-/s\n", - "\t TRAPPIST-1 1.279131256718357e+16 thousand e-/s\n" + "\t TRAPPIST-1 6.5 thousand e-/s\n" ] } ], @@ -847,10 +818,8 @@ }, { "cell_type": "code", - "execution_count": 88, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "hatp11_r_apo = 34000000\n", @@ -861,10 +830,8 @@ }, { "cell_type": "code", - "execution_count": 89, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -897,29 +864,29 @@ }, { "cell_type": "code", - "execution_count": 90, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 90, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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WEhKk11+Xjh+XrPV9f/1133jRA+ou1KFDh5wnCrds2VKtW7d2to0xSkxMVHx8vG666SYd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\nMyscFzhmZmZWOP8PHxU7ipcj/pMAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -946,21 +913,12 @@ "\n", "plt.legend()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -974,7 +932,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.1" } }, "nbformat": 4, From beed31b0b1ac672971a0bf9542a933e496c4cac9 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Thu, 13 Jun 2019 17:08:09 -0400 Subject: [PATCH 15/26] small changes made for consistancy --- observing_examples.ipynb | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 21adefc..0efd8c0 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -319,8 +319,7 @@ " plt.plot(bp.waveset, bp(bp.waveset), label=band)\n", "plt.legend(loc='lower right')\n", "plt.xlim(3000, 14000)\n", - "plt.show()\n", - " " + "plt.show()" ] }, { @@ -679,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -694,8 +693,7 @@ "source": [ "band = 'zprime'\n", "\n", - "observation = Observation(photlam_trappist1, \n", - " bp_atmos_qe[band])\n", + "observation = Observation(photlam_trappist1, bp_atmos_qe[band])\n", "\n", "countrate = observation.countrate(area=aperture_area) / gain\n", "counts_trappist1 = countrate * 10 * u.s\n", @@ -753,13 +751,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", " 'trappist1': {'spectrum': photlam_trappist1}\n", " }\n", + "\n", "for star in stars.keys():\n", " stars[star]['observation'] = Observation(stars[star]['spectrum'], \n", " kepler_response,\n", @@ -776,7 +775,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -791,11 +790,10 @@ ], "source": [ "kepler_area = np.pi * (1.4 * u.m / 2) ** 2 # area of Kepler's primary mirror\n", - "waverange = (kepler_response.waveset[0], kepler_response.waveset[-1])\n", "\n", "for star in stars.keys():\n", - " stars[star]['countrate'] = stars[star]['observation'].countrate(area=kepler_area,\n", - " waverange=waverange)\n", + " stars[star]['countrate'] = stars[star]['observation'].countrate(area=kepler_area)\n", + " \n", "print('synphot count rates:')\n", "print('\\t HAT-P-11:', round(stars['hatp11']['countrate'].value / 1e6, 1), 'million e-/s')\n", "print('\\t TRAPPIST-1', round(stars['trappist1']['countrate'].value / 1e3, 1), 'thousand e-/s')" @@ -813,7 +811,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We know from observations using AGILE on APO's 3.5m telescope that the actual count values for these stars taken on certain dates are:" + "We know from real observations (with AGILE on APO's 3.5m telescope and with Kepler) that the actual count values for these stars taken on certain dates are:" ] }, { From c700ee94f2d6fec5546ba568714cb2075e723cb5 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Thu, 13 Jun 2019 19:18:10 -0400 Subject: [PATCH 16/26] Added a query to SkyCalc for modeling the atmospheric transmission (not finished) --- observing_examples.ipynb | 76 +++++++++++++++++++++++++++++++++++----- 1 file changed, 67 insertions(+), 9 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 0efd8c0..fad1866 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -334,21 +334,79 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Query from Cerro Paranal" + "Model the sky by querying results from the SkyCalc Sky Model Calculator:" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'airmass': '1.0', 'pwv_mode': 'pwv', 'season': '0', 'time': '0', 'pwv': '3.5', 'msolflux': '130.0', 'incl_moon': 'Y', 'moon_sun_sep': '90.0', 'moon_target_sep': '45.0', 'moon_alt': '45.0', 'moon_earth_dist': '1.0', 'incl_starlight': 'Y', 'incl_zodiacal': 'Y', 'ecl_lon': '135.0', 'ecl_lat': '90.0', 'incl_loweratm': 'Y', 'incl_upperatm': 'Y', 'incl_airglow': 'Y', 'incl_therm': 'N', 'therm_t1': '0.0', 'therm_e1': '0.0', 'therm_t2': '0.0', 'therm_e2': '0.0', 'therm_t3': '0.0', 'therm_e3': '0.0', 'vacair': 'vac', 'wmin': '300.0', 'wmax': '2000.0', 'wgrid_mode': 'fixed_wavelength_step', 'wdelta': '0.1', 'wres': '20000', 'lsf_type': 'none', 'lsf_gauss_fwhm': '5.0', 'lsf_boxcar_fwhm': '5.0', 'observatory': '2640'}\n" + ] + }, + { + "ename": "JSONDecodeError", + "evalue": "Expecting value: line 1 column 1 (char 0)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mserver\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/observing/etc/api/skycalc'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 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"\n", - "# Make a transmission function from it\n", - "# Don't write the output to a file, just let them be arrays" + "import json\n", + "import requests\n", + "\n", + "# SkyCalc recognizes Cerro Paranal by its id number 2460, which we \n", + "# got from the skycalc_cli source code\n", + "paranal_id = '2640'\n", + "\n", + "# Set up parameters for the observing night in the format that skycalc_cli would use\n", + "params_path = download_file('https://www.eso.org/observing/etc/' + \n", + " 'doc/skycalc/skycalc_defaults.txt')\n", + "params = {}\n", + "with open(params_path) as o:\n", + " for row in o.readlines():\n", + " columns = row.split(':')\n", + " columns = [column.strip() for column in columns]\n", + " key, value = columns\n", + " params[key] = value\n", + " \n", + " if key == \"observatory\":\n", + " params['observatory'] = paranal_id\n", + "\n", + "print(params)\n", + " \n", + "# Use the bit from skycalc_cli which queries from the SkyCalc Sky Model\n", + "server = 'http://etimecalret-001.eso.org'\n", + "url = server + '/observing/etc/api/skycalc'\n", + "response = requests.post(url, data=json.dumps(params))\n", + "results = json.loads(response.text)\n", + "\n", + "status = results['status']\n", + "tmpdir = results['tmpdir']\n", + "tmpurl = server + '/observing/etc/tmp/' + \\\n", + " tmpdir + '/skytable.fits'\n", + "\n", + "if status == 'success':\n", + " try: \n", + " response = requests.get(tmpurl, stream=True)\n", + " data = response.content\n", + " except requests.exceptions.RequestException as e:\n", + " print(e, 'could not retrieve FITS data from server')\n", + "else:\n", + " print('HTML request failed')" ] }, { From a8049edfa57d067b0a314b79ee6e98389b747054 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Thu, 13 Jun 2019 19:41:54 -0400 Subject: [PATCH 17/26] not sure what to do with skycalc results? --- observing_examples.ipynb | 39 ++++++++++++++++++++------------------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index fad1866..b110a23 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -339,28 +339,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'airmass': '1.0', 'pwv_mode': 'pwv', 'season': '0', 'time': '0', 'pwv': '3.5', 'msolflux': '130.0', 'incl_moon': 'Y', 'moon_sun_sep': '90.0', 'moon_target_sep': '45.0', 'moon_alt': '45.0', 'moon_earth_dist': '1.0', 'incl_starlight': 'Y', 'incl_zodiacal': 'Y', 'ecl_lon': '135.0', 'ecl_lat': '90.0', 'incl_loweratm': 'Y', 'incl_upperatm': 'Y', 'incl_airglow': 'Y', 'incl_therm': 'N', 'therm_t1': '0.0', 'therm_e1': '0.0', 'therm_t2': '0.0', 'therm_e2': '0.0', 'therm_t3': '0.0', 'therm_e3': '0.0', 'vacair': 'vac', 'wmin': '300.0', 'wmax': '2000.0', 'wgrid_mode': 'fixed_wavelength_step', 'wdelta': '0.1', 'wres': '20000', 'lsf_type': 'none', 'lsf_gauss_fwhm': '5.0', 'lsf_boxcar_fwhm': '5.0', 'observatory': '2640'}\n" - ] - }, - { - "ename": "JSONDecodeError", - "evalue": "Expecting value: line 1 column 1 (char 0)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mserver\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/observing/etc/api/skycalc'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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"73\n", + "77\n", + "80\n", + "76\n" ] } ], @@ -381,12 +371,20 @@ " columns = row.split(':')\n", " columns = [column.strip() for column in columns]\n", " key, value = columns\n", + " \n", + " # convert numbered values to floats\n", + " try:\n", + " value = int(value)\n", + " except:\n", + " try:\n", + " value = float(value)\n", + " except:\n", + " pass\n", + " \n", " params[key] = value\n", " \n", " if key == \"observatory\":\n", " params['observatory'] = paranal_id\n", - "\n", - "print(params)\n", " \n", "# Use the bit from skycalc_cli which queries from the SkyCalc Sky Model\n", "server = 'http://etimecalret-001.eso.org'\n", @@ -406,7 +404,10 @@ " except requests.exceptions.RequestException as e:\n", " print(e, 'could not retrieve FITS data from server')\n", "else:\n", - " print('HTML request failed')" + " print('HTML request failed', results)\n", + " \n", + "for row in data[:5]:\n", + " print(row) # ???" ] }, { From 6021ffa97cd466d38551a8932c9c2e1d5513e0a2 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Fri, 14 Jun 2019 10:59:59 -0400 Subject: [PATCH 18/26] Skycalc query fully implemented --- observing_examples.ipynb | 145 ++++++++++++++------------------------- 1 file changed, 51 insertions(+), 94 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index b110a23..7098542 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -141,9 +141,7 @@ "WARNING: W06: None:177:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", "WARNING: W06: None:192:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", "WARNING: W06: None:204:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", - "WARNING: W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", - "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", - "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" + "WARNING" ] }, { @@ -152,6 +150,15 @@ "text": [ "Query finished.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ": W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" + ] } ], "source": [ @@ -339,30 +346,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 59, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "83\n", - "73\n", - "77\n", - "80\n", - "76\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "import requests\n", + "from astropy.io import fits\n", "\n", "# SkyCalc recognizes Cerro Paranal by its id number 2460, which we \n", "# got from the skycalc_cli source code\n", "paranal_id = '2640'\n", "\n", - "# Set up parameters for the observing night in the format that skycalc_cli would use\n", + "# Use the default parameters from skycalc to set up the parameter dict\n", "params_path = download_file('https://www.eso.org/observing/etc/' + \n", " 'doc/skycalc/skycalc_defaults.txt')\n", "params = {}\n", @@ -385,6 +381,9 @@ " \n", " if key == \"observatory\":\n", " params['observatory'] = paranal_id\n", + " \n", + "# edit the parameters to your observation\n", + "params['airmass'] = 1.5\n", " \n", "# Use the bit from skycalc_cli which queries from the SkyCalc Sky Model\n", "server = 'http://etimecalret-001.eso.org'\n", @@ -404,66 +403,28 @@ " except requests.exceptions.RequestException as e:\n", " print(e, 'could not retrieve FITS data from server')\n", "else:\n", - " print('HTML request failed', results)\n", - " \n", - "for row in data[:5]:\n", - " print(row) # ???" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cells show how to simulate an atmospheric transmission profile at a given site and time of year. We use `pwv_kpno`, a package that downloads data collected by SuomiNet on the precipitable water vapor in the atmosphere on a given night at the given location, and uses them to calculate the transmission as a function of wavelength. The list of sites from which data is collected can be seen here.\n", - "\n", - "This requires versions > 1.0.1 of `pwv_kpno`." + " print('HTML request failed', results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The next cell demonstrates how to configure a `pwv_kpno` observation site other than the default (aka Kitt Peak). Once this cell has been ran, it does not need to be ran again." + "Write the data requested from the SkyCalc site to a fits file so it can be read in a human readable format:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 36, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2018]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "from pwv_kpno.package_settings import ConfigBuilder, settings\n", - "from pwv_kpno import pwv_atm\n", - "from datetime import datetime\n", - "import pytz\n", - "import os\n", - "\n", - "if not os.path.isfile('./apache_point.ecsv'):\n", - " # configure the new observation site\n", - " new_config = ConfigBuilder(\n", - " site_name = \"apache_point\",\n", - " primary_rec=\"P027\", # the suominet receiver nearest APO\n", - " sup_rec = []\n", - " )\n", - " new_config.save_to_ecsv('./apache_point.ecsv')\n", - " settings.import_site_config('./apache_point.ecsv')\n", - "\n", - "# change site from KPNO to APO\n", - "settings.set_site('apache_point')\n", - "\n", - "# download data from SuomiNet from year observation was taken\n", - "pwv_atm.update_models(years=[2018])" + "with open('data.fits', 'wb') as f:\n", + " f.write(data)\n", + " \n", + "hdu = fits.open('data.fits')\n", + "trans_waves = hdu[1].data[\"LAM\"] * u.um # wavelengths\n", + "transmission = hdu[1].data[\"TRANS\"]" ] }, { @@ -475,12 +436,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -492,13 +453,9 @@ } ], "source": [ - "obsv_date = datetime(year=2018, month=5, day=15, # set the date of observation, let's say mid-May\n", - " tzinfo = pytz.utc)\n", - "transmission = pwv_atm.trans_for_date(date=obsv_date, airmass=1.5) # get the transmission function\n", - "\n", "atmosphere = SpectralElement(Empirical1D,\n", - " points=transmission['wavelength'],\n", - " lookup_table=transmission['transmission'])\n", + " points=trans_waves,\n", + " lookup_table=transmission)\n", "atmosphere.plot()" ] }, @@ -519,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -559,12 +516,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -592,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -618,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -634,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -642,7 +599,7 @@ "output_type": "stream", "text": [ "HAT-P-11 counts in the r-band, 10 s exposure:\n", - "44209346 cts\n" + "38353638 cts\n" ] } ], @@ -672,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -698,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 57, "metadata": { "scrolled": true }, @@ -737,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -745,7 +702,7 @@ "output_type": "stream", "text": [ "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "306440 cts\n" + "306750 cts\n" ] } ], @@ -779,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -810,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -834,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -875,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -887,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -921,22 +878,22 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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aMXny5GyvgL2cjDGrrLXBF9vPJ47M5NWcORcuMpCxffbsy5PHU8ybN8+lIiMiIt7NWsubb77JCy+8QOfOnYmOjna8yOSGJyxn4LidO51O4P1CQ0MJDQ11OoaIiOSStZaXX36Zt99+m27dujFu3DgKFy7sdKxc8YkjM/l5B2AREZGCwlrLc889x9tvv81jjz3G+PHjva7IgI+Umfy8A7CIiEhBYK2lf//+DBs2jH79+vHJJ59QqJB31gLvTC0iIiKXLD09nccff5yRI0fy9NNPM3LkSK8tMqAyc7YtWzIubSpdGgoVyvjet2/GuIiIiA9IS0ujZ8+ejBkzhhdeeIGhQ4dmexNTb6Iykyk2FgIDYdw4OHIErM34Pm5cxvjpm73l1oEDB7LuxHvddddRsWLFrMfGGIKCgqhZsyYtW7bk0KFDZz33gw8+oHjx4pw5FyghIQF/f3/q1KlD9erVeeONNy46HhUVBcDff/9NVFQUtWvXpkaNGrRo0YL169dn5SlbtixVq1YlKCiI8PDw8z5LUlISdevWpUiRIsTExFzSz+Ny2759OzVr1nQ6hoiIR0hNTeWRRx5h4sSJvPbaawwZMsTriwzoaqYMW7ZAu3Zw/Pj521JSMr7atYN166BatVy9dLly5Vi7di0Ar7/+OqVKleLZZ58FoFSpUlnbHnnkEUaPHs1LL72U9dzo6Gjq1avHrFmz6NatW9b4vffey7x58zh27BhBQUFZZSWn8UyvvvoqERER9O/fH4B169ZRq1atrAzdunUjKiqKdu3aZftZKlWqxGeffcbQoUNz9TO4mNTU1Kz1nURExD1SUlLo0qUL06ZNY8iQIbz44otOR8o3PnFkJs9XMw0bllFYLiQlBT744NJe3wX169dn165dWY+3bNnC0aNHeeutt3Jc3qBkyZLccccdbDnnNFhO43v27CEgICDrcWBgYK4yVqlShcDAwFydVx0/fjy33HILoaGhPPbYYzzxxBNARnF6+umnady4MQMHDuTgwYPcf//9BAYGcvfdd7Nu3TogowCeWZ5q1qyZtfRD9erVeeyxx7j99tuJjIzkxIkTAKxatYratWtTv359RhekuxyKiOTg1KlTdOjQgWnTpjF06FCfKjLgI2Umz1czTZrkWpn58stLe/2LSEtLIy4ujlatWmWNRUdH06lTJ+699142b97M3r17z3vegQMH+P7777n99ttdGu/Xrx89evSgcePGDBkyJN8XoDzX7t27GTx4MN9//z1LlizJWsMp06+//srSpUsZNmwYr732GnXq1GHdunW8/fbbPPzwwxd9/d9++41+/frx888/c9VVV2Ut0Pnoo48yYsQIvvvuO7d8LhERb3Ly5Enatm3LrFmz+PDDD3nmmWecjpTvfKLM5NnRo/m7n4tOnDhBUFAQ5cqV4+DBg0RERGRtmzJlCh07dqRQoUK0bduW6dOnZ2375ptvqFOnDpGRkQwaNCirtOQ0nqlp06Zs3bqVxx57jE2bNlGnTh327duXr5/pTD/++CMhISGULVsWPz8/2rdvf9b29u3bZ93PYPny5XTt2hWAsLAwDhw4wMWOtGXO7wG444472L59O8nJyRw6dIiQkBCArNcUESmIjh8/TuvWrZk/fz6ffPIJTz31lNOR3EITFQBKlcqY7OvKfvmoRIkSrF27luTkZKKiohg9ejRPPfUU69at47fffssqN6dOneLGG2+kX79+wP+fG3OunMbPVLZsWTp37kznzp2JiooiKSmJBx54INt9X3rpJebPnw+QNa8mNy627lfJkiUvuK8xhiJFipB+xnLmJ0+ezPpzsWLFsv5cuHBhTpw4gbXWJyaziYjk1bFjx2jZsiUJCQlMmDCBRx991OlIbqMjMwBdusDF1qDw8wM3/Svf39+fESNGMHToUFJSUoiOjub111/Pmhuye/dudu3axR9//JGn94mPj+f46UnOR44cYcuWLVSqVCnH/YcMGcLatWtdKjK33XbbeWN33nkniYmJ/PPPP6SmpmadBspOo0aN+Oqrr4CMK7CuvvpqSpcuTZUqVVi9ejUAq1evZtu2bRfMcdVVV+Hv78/y5csBsl5TRKQgOXLkCM2aNSMxMZEvvvjCp4sMqMxkeOYZ18rMgAFui1CnTh1q167NlClTmDJlCm3atDlre5s2bZgyZUqe3mPVqlUEBwcTGBhI/fr16dmzJ/Xq1XP5+StWrCAgIIDp06fTu3fvrNNY+/fvz/bISsWKFXnxxRe56667CA8Pp0aNGuQ0r+n1119n5cqVBAYGMmjQID7//HMAHnjgAQ4ePEhQUBAff/wxt9xyy0VzTpw4kX79+lG/fn1KlCjh8ucTEfEFhw4dIjIyku+++47o6Gi6dOnidCS3Mxc7FeBNgoOD7cqVK88a++WXX6hevfrFnxwbm3H5deal2Jn8/DK+YmKgefN8Tuwb5s2bx9atW7M9F3v06FFKlSpFamoqbdq0oXv37ucVNae4/LshIuIlDh48SGRkJOvWrWPatGncf//9TkfKE2PMKmtt8MX284kjM/my0GTz5hn3kenV6+w7APfqlTGuIpOjqKioHCeVvf7661k3BqxatarX/8USEXGnvNyIfv/+/YSFhbFhwwZmzZpVoP57qyMzUmDpd0NEPEleThD89ddfhIeHs2XLFmbPnk1kZOTlCe1mBerIjIiIiDc780b05972LCUlY7xdu+yP0OzatYvQ0FC2bdvGggULfKbI5IbKjIiIiMMu9Ub0f/75JyEhIezatYtFixbRuHFj94X0YCozIiIiDruUG9Fv27aNkJAQ9u/fz5IlS7jnnnvcG9KDqcxkZ8+ejAUl//rL6SQiIlIA5PZG9L///jshISEkJycTFxfH3Xff7b5wXkBlJjuDB8P27Rnf8+jAgQMEBQURFBTEddddR8WKFbMeG2OyrvRp2bIlhw4dOuu5H3zwAcWLFz/rtv4JCQn4+/tTp04dqlevzhtvvHHR8czVs//++2+ioqKoXbs2NWrUoEWLFqxfvz4rT9myZbOWCAgPDz/vsyQlJVG3bl2KFClCTExMnn82IiKSwdUbzJcqBZs2baJRo0acOHGCZcuWcccdd7g3nBdQmTnXnj0wcSKkp2d8z+PRmXLlymXdRbdPnz4MGDAg63HJkiVZu3YtGzZsoGzZsuet8BwdHU29evWYNWvWWeP33nsva9asYeXKlUyaNIlVq1ZdcDzTq6++SkREBD/99BMbN27k3XffpVatWll5WrVqxfvvv8/atWtZunTpeZ+lUqVKfPbZZ3Tu3DlPPxMRETmbqzeib9FiAyEhIaSnp5OQkEDt2rUvT0AP5xNlJl/uM5Np8OCMIgOQlpYvR2dcUb9+fXbt2pX1eMuWLRw9epS33nqL6OjobJ9TsmRJ7rjjDracM709p/E9e/YQEBCQ9TgwMDBXGatUqUJgYCCFCrn2a5OWlka3bt2oWbMmtWrV4oPTM9dCQ0P5z3/+Q4MGDahZsyY//vgjkLEwZYMGDahTpw4NGjRg8+bNWa/z7LPPUqtWLQIDAxk5ciSQcUfjkJAQ7rjjDpo2bcqePXty9XlERDyFKzeiL1x4LYsWhVKkSBESExPPW0y4IPOJMmOtnWut7ZXTrfJdlnlU5tSpjMenTuXL0ZmLSUtLIy4ujlatWmWNRUdH06lTJ+699142b97M3r17z3vegQMH+P7778/7hc5pvF+/fvTo0YPGjRszZMgQdu/e7Z4PdNratWvZtWsXGzZsYP369WetDXLs2DG+/fZbPvroI7p37w5krO+UlJTEmjVrePPNN3nxxRcB+PTTT9m2bRtr1qxh3bp1PPTQQ6SkpPDkk08SExPDqlWr6N69Oy+99JJbP4+IiLtUq5ZxH5krrji/1Pj5QbFiKylSJIxSpa4gMTGRW2+91ZmgHsonyky+OfOoTCY3Hp05ceIEQUFBlCtXjoMHD2atkg0wZcoUOnbsSKFChWjbti3Tp0/P2vbNN99Qp04dIiMjGTRoUFZpyWk8U9OmTdm6dSuPPfYYmzZtok6dOuzbt88tnw3gxhtvZOvWrTz55JMsXLiQ0qVLZ23r1KkTkLHA5OHDhzl06BDJycm0b9+emjVrMmDAAH7++WcAli5dSp8+fShSJGOR97Jly7J582Y2bNhAREQEQUFBvPXWW+zcudNtn0VExN1yuhF9q1bf4+fXhKuv9icpKYmbbrrJ6ageR2Um07lHZTK58ehMiRIlWLt2LX/88QenTp3KmjOzbt06fvvtNyIiIqhSpQpTpkw561RT5tyYVatW0adPn4uOn6ls2bJ07tyZL7/8knr16pGUlJRjvpdeeilrcvClKFOmDD/99BOhoaGMHj2anj17Zm0zxpy1rzGGV155hcaNG7Nhwwbmzp3LyZMnAbDWnre/tZbbb789a77P+vXrWbx48SXlFBHxFNWqwahRkJyc8W/pefO+YdGiCK677hqSkpKoUqWK0xE9kspMpuyOymRy89wZf39/RowYwdChQ0lJSSE6OprXX3+d7du3s337dnbv3s2uXbv4448/8vQ+8fHxHD9+HMhYHn7Lli1UqlQpx/2HDBmSVRYu5rbbbjtvbP/+/aSnp/PAAw8wePBgVq9enbVt6tSpACxfvhx/f3/8/f1JTk6mYsWKAHz22WdZ+0ZGRvLJJ5+QmpoKZCykduutt7Jv3z6+++47AFJSUrKO5IiI+IL4+HiaNWtGxYoVSUxM5IYbbnA6ksdSmYGcj8pkugxzZ+rUqUPt2rWZMmUKU6ZMOW9l6TZt2jBlypQ8vceqVasIDg4mMDCQ+vXr07NnT+rVq+fy81esWEFAQADTp0+nd+/eWaex9u/fT3ZrfGXeYjsoKIhu3brxzjvvZG0rU6YMDRo0oE+fPowfPx6A559/nhdeeIGGDRuSlpaWtW/Pnj2pVKkSgYGB1K5dm8mTJ1O0aFFiYmIYOHAgtWvXJigoiG+//fZSfzQiIh5l8eLF3HfffVStWpXExESuv/56pyN5NC00CRlLko4fn3OZAShaFHr2hHMunxaYN28eW7duzXHl7HOFhoYydOhQgoMvunaYW2mhSRHxRPPnz6dt27ZUr16dJUuWUL58eacjOcbVhSaLXI4wHm/OnAsXGcjYPnu2ykw2Mm/KJyIieTNr1iw6dOhAYGAgixcvpmzZsk5H8goqMwC6CuaySkhIcDqCiIjHmTZtGp07d6ZevXrExsZy1VVXOR3JaxSIOTO+dCpN8od+J0TEk0yaNIlOnTpRv359Fi1apCKTSz5fZooXL86BAwf0Py/JYq3lwIEDFC9e3OkoIiJMnDiRhx9+mEaNGhEbG3vWPbnENT5/mikgIICdO3e69eZw4n2KFy9+1tIOIiJOGDNmDH369CEiIoKvv/6aK664wulIXsknyowxpiXQMru7Ivr5+VG1atXLH0pEROQCRo4cyVNPPUWLFi2YMWOGjhbngU+cZsq3tZlEREQug2HDhvHUU0/RunVrZs6cqSKTRz5RZkRERLzF22+/zbPPPkv79u2ZPn06xYoVczqS11OZERERuQystbz++uu89NJLPPTQQ0yePBm/c5fIlkviE3NmREREPJm1lpdeeol33nmHbt26MW7cOAoXLux0LJ+hMiMiIuJG1lqeffZZhg8fTu/evfnoo48oVEgnRvKTfpoiIiJukp6ezlNPPcXw4cN58skn+fjjj1Vk3EBHZkRERNwgPT2dPn36MHbsWJ555hnef/99jDFOx/JJqociIiL5LC0tjR49ejB27FhefPFFFRk305EZERGRfJSamsojjzzC5MmTeeONN3jllVdUZNxMZUZERCSfpKSk8NBDDzF9+nTeeecdBg0a5HSkAkFlRkREJB/8+++/dOjQgdmzZzN8+HAGDBjgdKQCQ2VGREQkj06ePMkDDzzAggULGDVqFP369XM6UoGiMiMiIpIHx48fp3Xr1sTFxTFmzBh69erldKQCR2VGRETkEh09epSWLVuSmJjIhAkT6Natm9ORCiSfuDTbGNPSGPNpcnKy01FERKSAOHz4MM2aNeObb75h0qRJKjIO8okyY62da63t5e/v73QUEREpAA4dOkRkZCQ//PADU6ZMoXPnzk5HKtB0mklERCQXDhw4QGStIwjnAAAgAElEQVRkJOvXrycmJobWrVs7HanAU5kRERFx0d69e4mIiGDz5s3Mnj2b5s2bOx1JUJkRERFxyZ49ewgPD2fbtm3MmzeP8PBwpyPJaSozIiIiF7Fr1y7CwsLYtWsXCxYsIDQ01OlIcgaVGRERkQv4448/CAsLY9++fSxatIiGDRs6HUnOoTIjIiKSg61btxIWFsahQ4dYsmQJd911l9ORJBsqMyIiItn47bffCAsL4/jx48THx1O3bl2nI0kOVGZERETO8csvvxAWFkZqairLli0jMDDQ6UhyAT5x0zwREZH8sn79ekJCQrDWkpiYqCLjBVRmRERETluzZg2NGzfGz8+PxMREatSo4XQkcYHKjIiICLBixQrCwsIoWbIkSUlJ3HrrrU5HEhepzIiISIH37bffEh4eTpkyZUhKSqJatWpOR5JcUJkREZECLSkpicjISK699lqSkpKoXLmy05Ekl1RmRESkwIqLi6NZs2bccMMNJCYmEhAQ4HQkuQQqMyIiUiAtWrSIqKgoqlWrRkJCAhUqVHA6klwilRkRESlw5s6dS6tWrbjttttYtmwZ1157rdORJA9UZkREpECZOXMmbdu2pXbt2sTHx3P11Vc7HUnySGVGREQKjClTpvDggw9Sr149lixZQpkyZZyOJPlAZUZERAqEL7/8koceeoiGDRuyaNEi/P39nY4k+URlRkREfN6ECRN45JFHCA0NZcGCBVx55ZVOR5J8pDIjIiI+7eOPP6ZHjx40bdqUefPmUbJkSacjST5TmREREZ/14Ycf0rdvX1q2bMnXX39NiRIlnI4kblDE6QA5McbcCzxERsYa1toGDkcSEREv8v777/P888/Ttm1boqOjKVq0qNORxE0u65EZY8wEY8xeY8yGc8abGWM2G2N+N8YMArDWfmOt7QPMAz6/nDlFRMS7vfXWWzz//PN06NCBKVOmqMj4uMt9mukzoNmZA8aYwsBooDlQA+hkjDlzzfXOQPTlCigiIt7LWsurr77KK6+8QteuXfnqq6/w8/NzOpa42WUtM9baJODgOcN3Ar9ba7daa08BU4DWAMaYSkCytfZwTq9pjOlljFlpjFm5b98+d0UXEREPZ61l0KBBDB48mB49ejBx4kQKFy7sdCy5DDxhAnBFYMcZj3eeHgPoAUy80JOttZ9aa4OttcHly5d3U0QREfFk1lqefvpp/vvf//L444/z6aefqsgUIJ4wAdhkM2YBrLWvXeYsIiLiZdLT03nyySf56KOP6N+/Px988AHGZPe/FvFVnnBkZidwwxmPA4DdDmUREREvkp6eTu/evfnoo494/vnnVWQKKE8oMyuAm40xVY0xRYGOwJzcvIAxpqUx5tPk5GS3BBQREc+TlpbGo48+yrhx43j55Zd59913VWQKqMt9aXY08B1wqzFmpzGmh7U2FXgCWAT8Akyz1v6cm9e11s611vbSOhsiIgVDamoqXbp04YsvvuDNN99k8ODBKjIF2GWdM2Ot7ZTD+AJgweXMIiIi3unUqVN07tyZGTNm8N577/H88887HUkc5gkTgEVERFzy77//0r59e+bOncsHH3zAf/7zH6cjiQdQmREREa9w4sQJ2rZty8KFCxk9ejR9+/Z1OpJ4CE+YAJxnmgAsIuLbjh8/TsuWLVm0aBFjx45VkZGz+ESZ0QRgERHfdfToUVq0aMGyZcv47LPP6Nmzp9ORxMPoNJOIiHis5ORkWrRowQ8//MBXX31Fx44dnY4kHkhlRkREPNI///xD06ZNWbNmDVOnTuWBBx5wOpJ4KJUZERHxOPv37ycyMpKff/6ZGTNm0KpVK6cjiQfziTkzmgAsIuI79u7dS1hYGBs3bmT27NkqMnJRPlFmNAFYRMQ37Nmzh9DQUH7//Xfmz59Ps2bNnI4kXkCnmURExCPs3LmTsLAwdu/eTWxsLCEhIU5HEi+hMiMiIo77448/CAsLY9++fSxevJgGDRo4HUm8iMqMiIg4auvWrTRu3JjDhw+zdOlS7rzzTqcjiZdRmREREcf8+uuvhIWFceLECeLi4qhbt67TkcQL+cQEYF3NJCLifTZu3EhISAinTp0iISFBRUYumU+UGV3NJCLiXdatW0doaCgACQkJ1KpVy9lA4tV8osyIiIj3WL16NY0bN6Zo0aIkJiZSo0YNpyOJl1OZERGRy+bHH3+kSZMmlCpVisTERG655RanI4kPUJkREZHL4ttvvyUiIoKyZcuSlJREtWrVnI4kPkJlRkRE3C4xMZHIyEiuvfZaEhMTqVy5stORxIeozIiIiFvFxcXRvHlzKlWqRGJiIgEBAU5HEh/jE2VGl2aLiHimhQsXEhUVxU033URCQgIVKlRwOpL4IJ8oM7o0W0TE88ydO5fWrVtTvXp1li1bxjXXXON0JPFRPlFmRETEs8yYMYO2bdsSFBREXFwc5cqVczqS+DCVGRERyVfR0dF06NCBO++8k8WLF1OmTBmnI4mPU5kREZF888UXX9ClSxcaNmzIwoUL0el/uRxUZkREJF+MHz+ebt260bhxYxYsWMCVV17pdCQpIFRmREQkzz766CN69uxJ06ZNmTt3LiVLlnQ6khQgKjMiIpIn//vf/+jXrx8tW7bk66+/pkSJEk5HkgLGJ8qM7jMjIuKM9957jwEDBvDAAw8QExNDsWLFnI4kBZBPlBndZ0ZE5PIbPHgwgwYNomPHjkyZMoWiRYs6HUkKKJ8oMyIicvlYa3nllVd49dVXefjhh5k0aRJFihRxOpYUYPrtExERl1lrGThwIO+//z49e/ZkzJgxFCqkfxeLs/QbKCIiLrHWMmDAAN5//3369u2rIiMeQ7+FIiJyUenp6fTr148PP/yQ//znP4waNUpFRjyGfhNFROSC0tLS6NWrFx9//DEDBw5k+PDhGGOcjiWSRWVGRERylJqayqOPPsr48eN59dVXeeedd1RkxONoArCIiGQrJSWFrl27MnXqVAYPHszLL7/sdCSRbKnMiIjIeU6dOkXHjh2ZNWsW//3vf3nuueecjiSSI5UZERE5y7///ku7du2YN28e//vf/+jfv7/TkUQuyCfmzGg5AxGR/HHixAlat27NvHnz+Pjjj1VkxCv4RJnRcgYiInl37NgxoqKiWLx4MePHj6dPnz5ORxJxiU4ziYgIR44c4b777uP//u//+OKLL+jSpYvTkURcpjIjIlLAJScn07x5c3788Ueuvnoy4eEdnI4kkis+cZpJREQuzT///ENERAQrV64kPHwa+/d3YPBgp1OJ5I7KjIhIAbV//37CwsL46aefGDduJomJbUlPh4kT4a+/nE4n4jqVGRGRAujvv/+mcePGbNq0iTlz5vD991Gkp2dsS0tDR2fEq6jMiIgUMLt37yY0NJQtW7Ywb948AgObMnEinDqVsf3UKR2dEe+iMiMiUoDs2LGDkJAQdu7cycKFC2nSpAmDB5N1VCaTjs6IN1GZEREpILZv305ISAh79+5l8eLFNGrUiD17OOuoTCYdnRFvojIjIlIAbNmyhZCQEP755x+WLl1K/fr1AbI9KpNJR2fEW6jMiIj4uM2bN9OoUSOOHTtGfHw89erVA8jxqEwmHZ0Rb6EyIyLiw37++WdCQkJITU1l2bJl1KlTJ2vbhY7KZNLRGfEGKjMiIj7qp59+IjQ0lEKFCpGQkECtWrXO2j5nTs5HZTKdOgWzZ7sxpEg+0HIGIiI+aPXq1URERHDFFVcQHx/PzTfffN4+O3c6EEzEDXRkRkTEx/zwww+EhYVx5ZVXkpSUlG2REfElPlFmjDEtjTGfJicnOx1FRMRRy5cvJyIignLlypGYmEjVqlWdjiTidj5RZqy1c621vfz9/Z2OIiLimISEBJo1a0aFChVISkqicuXKTkcSuSx8osyIiBR0S5cupUWLFlSuXJnExEQqVqzodCSRy0ZlRkTEyy1YsICoqChuvvlmEhISuO6665yOJHJZqcyIiHix2bNnc//993P77bcTHx9P+fLlnY4kctmpzIiIeKnp06fTrl076tSpQ1xcHOXKlXM6kogjVGZERLzQ5MmT6dixI3fddRdLlizhqquucjqSiGNUZkREvMznn39Oly5daNSoEQsXLqR06dJORxJxlMqMiIgXGTt2LI8++ijh4eHMnz+fUqVKOR1JxHEqMyIiXmL06NH06tWL5s2bM2fOHK644gqnI4l4BJUZEREvMHz4cJ544glat27NzJkzKV68uNORRDyGyoyIiId79913eeaZZ2jXrh3Tp0+nWLFiTkcS8SgqMyIiHspay5tvvskLL7xA586diY6Oxs/Pz+lYIh6niNMBRETkfNZaXn75Zd5++20eeeQRxo8fT+HChZ2OJeKRVGZERDyMtZbnnnuOYcOG8dhjj/HJJ59QqJAOpIvkRH87REQ8iLWW/v37M2zYMPr166ciI+IC/Q0REfEQ6enpPP7444wcOZKnn36akSNHqsiIuEB/S0REPEBaWho9e/ZkzJgxvPDCCwwdOhRjjNOxRLyC5syIiDgsNTWVbt268dVXX/Haa6/x2muvqciI5ILKjIiIg1JSUnjooYeYPn06Q4YM4cUXX3Q6kojXcek0kzHmQWNM5BmPXzXG7DTGLDLGVHBfPBER33Xq1CkefPBBpk+fztChQ1VkRC6Rq3NmXs/8gzGmLvAiMALwA4blfywREd928uRJ2rZty9dff82IESN45plnnI4k4rVcPc1UGdh8+s9tgK+ttf81xiwGFrklmYiIjzp+/Dht2rRh8eLFfPLJJ/Tu3dvpSCJezdUjMyeBK0//uQmw9PSfk88YFxGRizh27BhRUVEsWbKECRMmqMiI5ANXj8x8AwwzxiwHgoF2p8dvAXa4I5gxphAwGCgNrLTWfu6O9xERuVyOHDlCixYt+Pbbb/niiy/o0qWL05FEfIKrR2aeAE6RUWL6WGt3nx5vTi5OMxljJhhj9hpjNpwz3swYs9kY87sxZtDp4dZARSAF2Onqe4iIeKJDhw4RGRnJd999R3R0tIqMSD5y6ciMtXYn0DKb8f/k8v0+A0YBX2QOGGMKA6OBCDJKywpjzBzgVuA7a+0YY0wMEJfL9xIR8QgHDx4kMjKSdevWERMTw/333+90JBGf4uql2VuNMeWyGb/KGLPV1Tez1iYBB88ZvhP43Vq71Vp7CphCxlGZncA/p/dJu0C2XsaYlcaYlfv27XM1iojIZbFv3z7CwsJYv349s2bNUpERcQNXTzNVAbJbe74YEJDHDBU5e97NztNjM4GmxpiRQFJOT7bWfmqtDbbWBpcvXz6PUURE8s9ff/1F48aN2bx5M3PnzuW+++5zOpKIT7rgaSZjTNszHt5njEk+43FhMq5s2pbHDNnds9taa48DPfL42iIijti9ezdhYWHs2LGD+fPnExYW5nQkEZ91sTkzMae/W2D8OdtSgO1AXu/0tBO44YzHAcDuHPYVEfF4O3bsICwsjL/++ouFCxdy7733Oh1JxKddsMxYawsBGGO2AfWstfvdkGEFcLMxpiqwC+gIdM7NCxhjWgItb7rpJjfEExFx3bZt2wgLC+PgwYMsWbKEu+++2+lIIj7PpTkz1tqq+VFkjDHRwHfArafXduphrU0l49LvRcAvwDRr7c+5eV1r7VxrbS9/f/+8RhQRuWS///47ISEhJCcnExcXpyIjcpm4vGq2MeYuMubIXMM5Jcha+5Qrr2Gt7ZTD+AJggatZREQ8zaZNmwgLCyMlJYX4+HiCgoKcjiRSYLhUZowxzwL/BX4nYz6LPWOzzfZJIiIFxIYNG2jSpAnGGJYtW0bNmjWdjiRSoLh6ZKY/8JS1dpQ7w4iIeJuffvqJ8PBw/Pz8iI+P57bbbnM6kkiB4+p9ZkrjwaeBjDEtjTGfJicnX3xnEZF8snLlSho3bkyJEiVISkpSkRFxiKtlJhpo5s4geaEJwCJyuX3//fc0adIEf39/EhMT0dWUIs5x9TTTDuANY0xDYB0Z95jJYq0dnt/BREQ81fLly2nevDnXXnst8fHxVKpUyelIIgWaq2WmJ3AUaHD660wWUJkRkQJh2bJlREVFccMNNxAfH8/111/vdCSRAs/VVbOrujuIiIinW7x4Ma1bt6ZatWrExcVx7bXXOh1JRHB9zoxH0wRgEXG3+fPn07JlS2699VaWLVumIiPiQVy9z8yIC2139aZ57mKtnQvMDQ4OfszJHCLim2bNmkWHDh0IDAxk8eLFlC1b1ulIInIGV+fM1DrnsR9w2+nnr87XRCIiHmTatGl07tyZevXqERsby1VXXeV0JBE5h6tzZhqfO2aMKU7GStrf5HcoERFP8NVXX/Hwww/ToEEDFixYwJVXXul0JBHJxiXPmbHWngSGAC/lXxwREc8wceJEunbtSkhICAsXLlSREfFgeZ0AXB4olR9BREQ8xZgxY+jevTvh4eHMmzePkiVLOh1JRC7A1QnAT587BFQAHsIDljkwxrQEWuoOnCKSVyNHjuSpp57ivvvuIyYmhuLFizsdSUQuwlh78UWvjTHbzhlKB/YB8cA71tojbsiWa8HBwXblypVOxxARLzVs2DCeffZZ7r//fqZOnUrRokWdjiRSoBljVllrgy+2n26aJyICvP3227z00ks8+OCDTJo0CT8/P6cjiYiLcj1nxhhTyhijE8gi4hOstbz++uu89NJLPPTQQ3z11VcqMiJexuUyY4zpZ4z5E0gGDhtj/jDG9HVfNBER97LW8uKLL/LGG2/QrVs3Pv/8c4oUcfX2WyLiKVydAPwi8AIwFFh+evhe4F1jTGlr7btuyici4hbWWp599lmGDx9O7969+eijjyhUyCdWeBEpcFz9J0gfoJe1NvqMsThjzG/A24DKjIh4jfT0dPr378+oUaN48skn+fDDDzHGOB1LRC6Rq/8MuQZYkc34j4Djq61poUkRcVV6ejp9+vRh1KhRPPPMMyoyIj7A1TLzK9A5m/HOwOb8i3NprLVzrbW9/P39nY4iIh4sLS2N7t27M3bsWF588UXef/99FRkRH+DqaabXgWnGmEbA/wEWuAcIAdq7J5qISP5JTU3lkUceYfLkybzxxhu88sorKjIiPsLV+8zMNMbcBQwAosi4A/BG4E5r7Ro35hMRybOUlBQ6d+5MTEwM77zzDoMGDXI6kojkI5evQbTWrgK6uDGLiEi++/fff+nQoQOzZ89m+PDhDBgwwOlIIpLPXL00uz1wylo7+5zx1oCftTbGHeFERPLi5MmTPPDAAyxYsIBRo0bRr18/pyOJiBu4OgH4deBkNuPHTm8TEfEox48fp2XLlsTGxjJmzBgVGREf5uppphvJ/qql309vExHxGEePHqVly5YkJiYyYcIEunXr5nQkEXEjV4/M/APcnM34LYDjK2brPjMikunw4cM0a9aMb775hkmTJqnIiBQArpaZ2cAHxphbMgeMMbcCw4Gv3REsN3SfGREBOHToEJGRkfzwww9ER0fTuXN2t8cSEV/japl5nowFJjcaY3YYY3YAPwOHgefcFU5ExFUHDhygSZMmrF69mpiYGNq31y2wRAoKV+8zcwRoaIyJAILIuM/MaiDOWmvdmE9E5KL27t1LREQEmzdv5uuvv6ZFixZORxKRyyhXa91ba5cAS9yURUQk1/766y+aNGnCtm3bmDt3LhEREU5HEpHLLFdlRkTEk+zatYuwsDB27drFggULCA0NdTqSiDjA1TkzIiKXbssW6NsXSpeGQoUyvvftmzF+if78809CQkLYs2cPixYtUpERKcBUZkTEvWJjITAQxo2DI0fA2ozv48ZljMfG5volt27dSqNGjdi/fz9LliyhYcOGbgguIt5CZUZE3GfLFmjXDo4fh5SUs7elpGSMt2uXqyM0v/32GyEhIRw5coS4uDjuuuuufA4tIt7GpTJjjNlqjCmXzfhVxpit+R9LRHzCsGHnl5hzpaTABx+49HK//PILISEhnDx5kvj4eO644458CCki3s7VIzNVgMLZjBcDKuZbGhHxLZMmuVZmvvzyoi+1YcMGQkNDSU9PJyEhgdq1a+dTSBHxdhe8mskY0/aMh/cZY85cL6Aw0ATY7oZcuWKMaQm0vOmmm5yOIiJnOno0X/Zbs2YNERERFCtWjPj4eG699dZ8CCcivsJc6J53xpj003+0ZNwo70wpZBSZZ6y189ySLpeCg4PtypUrnY4hIplKl86Y7OvKfjmsrbZixQoiIyO58soriY+PR/9oESk4jDGrrLXBF9vvgqeZrLWFrLWFgD+BazIfn/4qZq291VOKjIh4oC5dwM/vwvv4+UHXrtlu+u677wgPD6dMmTIkJSWpyIhItlyaM2OtrWqt3e/uMCLiY555xrUyM2DAecNJSUlERkZy7bXXkpiYSJUqVdyTUUS8nsuXZhtj7jPGJBlj9htj9hljEo0xWgBFRHJWrRrExMAVV5xfavz8MsZjYjL2O0NcXBzNmzcnICCAhIQEbrjhhssYWkS8jauXZvcEZgFbgIHAIGAbMMsY09198UTE6zVvDuvWQa9eZ98BuFevjPHmzc/afdGiRURFRXHjjTeSkJDA9ddf71BwEfEWF5wAnLWTMb8BH1prR50z/iTwpLX2FjflyxVNABbxbnPnzqVdu3bUqFGDJUuWcPXVVzsdSUQclC8TgM9QCViYzXgsUDk3wUREsjNz5kzatm1LYGAgcXFxKjIi4jJXy8yfQEQ245HAH/kXR0QKoqlTp/Lggw9Sr149li5dStmyZZ2OJCJe5II3zTvDUGCkMaYu8C0Z9525B+gKPOmmbCJSAHz55Zd069aNhg0bMn/+fK688kqnI4mIl3GpzFhrxxhj9gLPAJl3Bf4FeNBaO9td4UTEt02YMIGePXvSuHFj5syZQ8mSJZ2OJCJeyNUjM1hrZ5FxRZOISJ598sknPP744zRt2pRZs2ZRokQJpyOJiJdyucxkMsZcxTlzbay1B/MtkYj4vBEjRtC/f3+ioqKYPn06xYsXdzqSiHgxV+8zU9kYE2uMOQkcAPad/tp/+ruIiEvef/99+vfvT5s2bZgxY4aKjIjkmatHZiYCVwHdgd1kTAAWEcmVt956i1deeYUOHTrw5Zdf4nexpQ5ERFzgapm5E7jbWrvBnWFExDdZa3nttdcYPHgwXbt2ZcKECRQpkuuz3CIi2XL1PjPbgGLuDJIXxpiWxphPk5OTnY4iIuew1vLCCy8wePBgunfvzsSJE1VkRCRfuVpm+gPvGGNucmeYS2WtnWut7eXv7+90FBE5g7WWp59+mvfee4/HH3+csWPHUrhwYadjiYiPyfGfR8aYI5w9N6Y4sNkY8y+Qeua+1trS7oknIt4qPT2dJ598ko8++oj+/fvzwQcfYIxxOpaI+KALHet94rKlEBGfkp6eTu/evRk3bhzPPfcc7733noqMiLhNjmXGWvv55QwiIr4hLS2N7t2788UXX/Dyyy/z5ptvqsiIiFu5PAvPGFMc6AzUOD20EYi21p5wRzAR8T6pqal07dqVKVOm8Oabb/LKK684HUlECgCXyszpBSbnkzFvZv3p4e7AEGPMfdba1W7KJyJe4tSpU3Tu3JkZM2bw7rvvMnDgQKcjiUgB4eqRmU+Bb4BHrbXHAIwxJYEJp7cFuyeeiHiDf//9l/bt2zN37lyGDx/OgAEDnI4kIgWIq2XmduDhzCIDYK09Zox5E1jplmQi4hVOnDhB27ZtWbhwIaNHj6Zv375ORxKRAsbV+8xsAq7PZrwC8Gv+xRERb3L8+HFatmzJokWLGDt2rIqMiDjC1SMzLwMjTh+J+f702N2nxwcZY8pm7qgVtEUKhqNHjxIVFcU333zDZ599xsMPP+x0JBEpoFwtM3NPf5/M/7+RXua1lrPPeGwB3d5TxMclJyfTokULfvjhByZNmkSnTp2cjiQiBZirZaaxW1OIiNf4559/aNq0KWvWrGHq1Kk88MADTkcSkQLOpTJjrU10dxAR8Xz79+8nMjKSn3/+mRkzZtCqVSunI4mI5OqmeVcAQcA1nDNx2Fo7M59ziYiH2bt3L+Hh4fz666/Mnj2bZs2aOR1JRARw/aZ54UA0UC6bzZonI+Lj9uzZQ5MmTdi+fTvz5s0jPDzc6UgiIllcvTT7QzLuABxgrS10zpeKjIgP27lzJyEhIfz555/ExsaqyIiIx3H1NFMVoJW1drcbs4iIh/njjz8ICwtj3759LFq0iIYNGzodSUTkPK4emfk/4FZ3BhERz7JlyxYaNWrEwYMHWbp0qYqMiHgsV4/MfAIMNcZcT8ZCkylnbtRCkyK+5ddffyUsLIwTJ04QFxdH3bp1nY4kIpIjV8tMzOnvn2azTROARXzIxo0badKkCWlpaSxbtozAwECnI4mIXJCrZaaqW1OIiEdYt24d4eHhFC5cmISEBGrUqOF0JBGRi3K1zBy31u5zaxIRcdTq1auJiIigRIkSxMfHc8sttzgdSUTEJa5OAN5ljJlhjGlujDEX311EvMmPP/5IkyZNKFWqFImJiSoyIuJVXC0z9wH/AjOAHcaYwcaYau6LBcaYUGPMN8aYT4wxoe58L5GC7NtvvyU8PJwyZcqQlJREtWpu/astIpLvXCoz1tol1trOwPXAO0Bz4FdjTLwx5iFjTHFXXscYM8EYs9cYs+Gc8WbGmM3GmN+NMYMy3xY4ChQHdrr6gUTEdYmJiURGRnLdddeRlJRE5cqVnY4kIpJrrh6ZAcBae8haO9paGww8BTQAvgR2G2PeNcaUushLfAactaCLMaYwMJqMglQD6GSMqQF8Y61tDgwE3shNThG5uKVLl9K8eXMqVapEYmIiAQEBTkcSEbkkuSozxpgKxphBxpjNwHvAFCAEeJyMkvL1hZ5vrU0CDp4zfCfwu7V2q7X21OnXbG2tTT+9/R+g2AUy9TLGrDTGrNy3T3OURVwRGxtLVFQUN910EwkJCVSoUMHpSCIilwXp+VwAACAASURBVMzVhSbbAt2BSGADGWs1TbLWHj5jn/XA2kvIUBHYccbjncBdp9+zKXAVMCqnJ1trP+X0/W+Cg4PtJby/SIEyZ84c2rdvz+23386SJUsoVy679WNFRLyHq5dmTyRj1ez61tpVOeyzDRhyCRmyuzrKWmtnAjMv4fVEJAczZsygY8eO1KlTh0WLFlGmTBmnI4mI5JmrZaaCtfb4hXaw1p7g0ua27ARuOONxAKAFLUXyWXR0NF27duWuu+5iwYIF+Pv7Ox1JRCRfuDpnpooxJmuhSWNMhDFmkjHmhdMTePNiBXCzMaaqMeb/tXfn4VHVd/vH3x/CKipuuKFSHqUqrTsulUchCUuQRBZFArKKICAqy4/Cj4KVguKCCEUW2amUfQ9LWAJJ3BWtqKC0aF1QylJqkB2S7/PHDG1ME5hAMmfmzP26rrnCnHPmzD1f5zpze5aZ8kAqsKw4KzCzFDObmJOTc4ZRRPxpxowZtG3bljp16pCenq4iIyK+EmqZmQLcAmBmVwBLgQuAx4FhoT6Zmc0G3gGuNbPtZtbZOXcc6AmsBj4H5jnnNof+EsA5l+ac66oNtMh/mzx5Mp06dSI+Pp6VK1dyzjnneB1JRKREhXqY6XrgxC9jtwTec87dZ2bxBM6n+f+hrMQ517qI6SuBlSFmEZEQjRs3jscff5ykpCQWLVpEpUqVvI4kIlLiQt0zEwccDf47kf8Ujy+BS0o6lIicuVGjRvH444+TkpLCkiVLVGRExLdCLTOfAd3N7B4CZSY9OL0asKc0ghWHzpkR+bkXXniB3r1788ADD7BgwQIqVCjyq5pERKJeqGWmP9AFyARmO+c+DU6/H3i/FHIVi86ZEfmPoUOHMmDAAFJTU5kzZw7ly5f3OpKISKkK6ZwZ51y2mVUFznXO/SvfrNeAk16yLSLh4Zzj6aefZtiwYbRv356pU6cSF3emFxuKiES+UE8AxjmXS+CnBfJP+7qkA4lI8Tnn6N+/Py+99BKPPvoor732GmXKFOvXSkREolbIZUZEIpNzjt69ezN69Gi6d+/Oq6++qiIjIjFFWzyRKJaXl0ePHj0YPXo0vXr1YuzYsSoyIhJzfLHV09VMEotyc3Pp0qULEyZMoH///owcORKzwn7qTETE33xRZnQ1k8Sa48eP07FjR6ZOncrgwYMZPny4ioyIxCydMyMSZY4dO0a7du2YO3cuQ4cOZdCgQV5HEhHxlMqMSBQ5evQoqampLF68mBdffJF+/fp5HUlExHMqMyJR4vDhw7Rs2ZLly5czatQonnrqKa8jiYhEBJUZkShw6NAhmjdvzurVqxk/fjzdunXzOpKISMTwxQnAuppJ/OzAgQMkJyezZs0apkyZoiIjIlKAL8qMrmYSv/rpp59o3LgxmZmZzJgxg0ceecTrSCIiEUeHmUQiVE5ODo0bN+b9999n1qxZtGrVyutIIiIRSWVGJALt3buXRo0asWnTJubNm0eLFi28jiQiErFUZkQizJ49e2jQoAFbtmxh0aJFJCcnex1JRCSiqcyIRJCdO3dSv359tm3bxrJly2jUqJHXkUREIp7KjEiE+OGHH0hMTOSbb75h+fLlJCYmeh1JRCQq+OJqJl2aLdHuu+++o27dumzfvp309HQVGRGRYvBFmdGl2RLNvv76a+rWrcuuXbtYs2YN9957r9eRRESiig4ziXjoyy+/JCEhgX379rFu3Tpuv/12ryOJiEQdlRkRj2zdupWEhASOHDnC+vXrueWWW7yOJCISlVRmRDywefNmEhMTcc6xYcMGbrjhBq8jiYhELV+cMyMSTTZt2kS9evUoU6YMmZmZKjIiImdIZUYkjD788EPi4+OpWLEiWVlZXH/99V5HEhGJeiozImHy3nvvkZiYyLnnnkt2djY1a9b0OpKIiC/4oszoe2Yk0r355ps0aNCACy+8kKysLGrUqOF1JBER3/BFmdH3zEgky8zMJCkpicsuu4zs7GyqV6/udSQREV/xRZkRiVRr167lvvvuo3r16mRlZVGtWjWvI4mI+I7KjEgpWblyJSkpKdSsWZPMzEwuvfRSryOJiPiSyoxIKVi6dCnNmjXjV7/6FevXr6dq1apeRxIR8S2VGZESNn/+fB588EFuueUWMjIyuPDCC72OJCLiayozIiVo1qxZpKamcuedd7J27VrOO+88ryOJiPieyoxICZkxYwZt27bl3nvvJT09nXPPPdfrSCIiMUFlRqQETJo0iU6dOpGYmMiKFSs4++yzvY4kIhIzVGZEztDYsWPp2rUrSUlJpKWlcdZZZ3kdSUQkpqjMiJyBkSNH0rNnT5o2bcrixYupWLGi15FERGKOL8qMfs5AvDB8+HD69u3Lgw8+yPz586lQoYLXkUREYpIvyox+zkDCyTnHkCFDGDhwIG3atGH27NmUK1fO61giIjGrrNcBRKKJc45Bgwbx3HPP0aFDB6ZMmUJcXJzXsUREYprKjEiInHP069ePl19+mS5dujBhwgTKlPHFzk0RkaimLbFICJxzPPXUU7z88ss8/vjjKjIiIhFEW2ORU8jLy6Nbt26MGTOGPn36MGbMGBUZEZEIoi2yyEnk5ubSuXNnJk6cyIABAxgxYgRm5nUsERHJR2VGpAjHjx+nQ4cOTJ8+nd///vc899xzKjIiIhFIJwCLFOLYsWM8/PDDzJ8/n2effZaBAwd6HUlERIqgMiNSwJEjR0hNTWXJkiWMGDGCvn37eh1JREROQmVGJJ/Dhw/z4IMPsmLFCv74xz/yxBNPeB1JREROQWVGJOjgwYM0b96cNWvWMGHCBB577DGvI4mISAhUZkSAAwcOkJKSQmZmJlOnTqVTp05eRxIRkRCpzEjM27dvH02aNOHtt9/mT3/6E23btvU6koiIFIPKjMS0H3/8kaSkJDZu3Mjs2bN56KGHvI4kIiLFpDIjMWvv3r00bNiQTz75hPnz59O8eXOvI4mIyGnwxZfmmVmKmU3MycnxOopEid27d5OQkMCnn37KokWLVGRERKKYL8qMcy7NOde1SpUqXkeRKPCPf/yD+Ph4tm7dSlpaGsnJyV5HEhGRM6DDTBJTvv/+exITE/nuu+9YsWIFCQkJXkcSEZEzpDIjMePbb78lISGBnTt3kp6ezj333ON1JBERKQEqMxIT/v73v5OQkMDevXtZu3Ytd911l9eRRESkhKjMiO9t27aNhIQE9u/fT0ZGBrVr1/Y6koiIlCCVGfG1L774goSEBI4dO8b69eu5+eabvY4kIiIlTGVGfOuzzz4jMTERM2PDhg38+te/9jqSiIiUAl9cmi1S0Mcff0y9evWIi4sjMzNTRUZExMdUZsR3Nm7cSEJCApUqVSIrK4vrrrvO60giIlKKVGbEV959910SExOpUqUK2dnZ1KxZ0+tIIiJSylRmxDfeeOMNGjRoQNWqVcnKyqJGjRpeRxIRkTBQmRFfWL9+PUlJSVSrVo3s7GyuuuoqryOJiEiYqMxI1FuzZg1NmjShRo0aZGVlcfnll3sdSUREwkhlRqLaihUrSElJ4dprr2XDhg1ccsklXkcSEZEwU5mRqLV48WKaN2/ODTfcwPr166latarXkURExAMqMxKV5s2bR8uWLbnttttYt24dF1xwgdeRRETEIyozEnVmzpxJ69at+c1vfsPq1as577zzvI4kIiIeUpmRqDJt2jTat29P3bp1WbVqFeeee67XkURExGMqMxI1XnvtNR555BHq16/P8uXLOfvss72OJCIiEUBlRqLCmDFj6NatG02aNGHZsmWcddZZXkcSEZEIoTIjEW/EiBE8+eSTNGvWjEWLFlGxYkWvI4mISARRmZGI9txzz9GvXz9atmzJvHnzKF++vNeRREQkwqjMSERyzvHMM8/wu9/9jocffphZs2ZRrlw5r2OJiEgEiugyY2aVzexDM0v2OouEj3OOgQMHMmTIEDp27MiMGTMoW7as17FERCRChbXMmNlUM9tlZp8VmJ5kZlvNbJuZDcg3qz8wL5wZxVvOOfr27cvzzz/PY489xpQpU4iLi/M6loiIRLBw75mZDiTln2BmccBYoDFQC2htZrXMrD6wBdgZ5ozikby8PJ544gleeeUVnnjiCcaPH0+ZMhG981BERCJAWPfdO+eyzewXBSbfAWxzzn0FYGZzgKbA2UBlAgXnkJmtdM7lFVynmXUFugJcddVVpRdeSlVeXh7dunVj0qRJ9O3bl5deegkz8zqWiIhEgUg4EaEa8F2++9uBO51zPQHMrCOwp7AiA+CcmwhMBKhdu7Yr3ahSGnJzc+ncuTMzZsxg4MCBDBs2TEVGRERCFgllprBPrX+XEufc9PBFkXA7fvw4HTp0YNasWQwZMoTBgweryIiISLFEQpnZDlyZ7/4VwA8eZZEwOnbsGG3atGHBggUMHz6cAQMGnPpBIiIiBUTC2ZUfADXNrIaZlQdSgWXFWYGZpZjZxJycnFIJKCXvyJEjtGzZkgULFvDyyy+ryIiIyGkL96XZs4F3gGvNbLuZdXbOHQd6AquBz4F5zrnNxVmvcy7NOde1SpUqJR9aStzhw4dp0aIFS5cuZcyYMfTp08frSCIiEsXCfTVT6yKmrwRWhjOLeOPgwYM0bdqUjIwMXnvtNbp27ep1JBERiXKRcM6MxIj9+/eTkpJCVlYWU6dOpWPHjl5HEhERH4iEc2bOmM6ZiXz79u0jKSmJ7OxsZs6cqSIjIiIlxhdlRufMRLYff/yRBg0a8N577zFnzhzatGnjdSQREfERHWaSUvXPf/6Thg0b8umnn7JgwQKaNm3qdSQREfEZX+yZEQ/s2AFXXw3/+EeRi+zatYuEhAQ2b97MkiVLVGRERKRUqMzI6Rk6FL7+OvC3EDt27CA+Pp6//e1vpKWlcd9994U3n4iIxAyVGSm+HTtg2jTIywv8LbB35vvvv6devXp88803rFy5kgYNGngUVEREYoEvyoyuZgqzoUMDRQYgN/dne2e+/fZb6taty44dO0hPT6devXreZBQRkZhhzvnnh6Zr167tNm7c6HUMf9uxA/7nf+Dw4f9Mq1QJvvqKrw4eJCEhgR9//JHVq1dz5513epdTRESinpl96JyrfarldDWTFE/+vTIn5Obyt759ScjO5uDBg2RkZHDbbbd5k09ERGKOLw4zSZicOFfm6NGfTf786FHunTWLwwcPsn79ehUZEREJK5UZCV0he2U+BeoCDshs2JCbbrrJi2QiIhLDVGYkNIXslfkLEA+UA7KAXy1detLvnRERESkNvigzupopDArslfkASADOIlBkroX/urJJREQkHHxRZvTbTGGwbNm/98q8DdQHzgeygWtOLHP0KCxd6kk8ERGJXb4oMxIG27eDc2RnZdGwcmUuvuYasr79ll84B/lv27d7nVRERGKMyoyELCMjg6SkJK688kqysrK48sorvY4kIiKiMiOhWb16NcnJyVx99dVkZmZy+eWXex1JREQEUJmREKSlpXH//fdz3XXXsWHDBi655BKvI4mIiPybyoyc1KJFi2jRogU33ngjGRkZXHTRRV5HEhER+RlflBldml065s6dy0MPPcTtt9/OunXruOCCC7yOJCIi8l98UWZ0aXbJe/3112nTpg133303q1evRmMrIiKRyhdlRkrW1KlT6dChA/Xq1WPVqlWcc845XkcSEREpksqM/Mz48ePp3LkzDRs2ZPny5VSuXNnrSCIiIielMiP/Nnr0aHr06EFycjJLliyhUqVKXkcSERE5JZUZAeCll16iV69eNG/enIULF1KxYkWvI4mIiIREZUYYNmwYv/3tb2nVqhVz586lfPnyXkcSEREJmcpMDHPO8fTTTzN48GDatWvHzJkzKVeunNexREREisUXZUbfM1N8zjkGDBjA0KFDeeSRR5g2bRply5b1OpaIiEix+aLM6Htmisc5R58+fXjxxRfp1q0bkyZNIi4uzutYIiIip8UXZUZCl5eXR8+ePRk1ahRPPvkk48aNo0wZvQ1ERCR66VMshuTl5fHYY48xbtw4+vXrx6hRozAzr2OJiIicEZWZGJGbm0unTp2YPHkygwYN4oUXXlCRERERX9AZnzHg2LFjtG/fnjlz5vCHP/yBwYMHex1JRESkxKjM+NzRo0dp06YNCxcu5Pnnn6d///5eRxIRESlRKjM+duTIEVq2bElaWhojR46kd+/eXkcSEREpcSozPnXo0CFatGhBeno6Y8eOpUePHl5HEhERKRUqMz504MABmjZtyvr165k0aRKPPvqo15FERERKjcqMz/z0008kJyfz5ptvMn36dNq3b+91JBERkVLli0uz9XMGATk5OTRq1Ii33nqLmTNnqsiIiEhM8EWZ0c8ZwL/+9S8aNGjABx98wNy5c2ndurXXkURERMJCh5l8YM+ePTRs2JDNmzezcOFC7r//fq8jiYiIhI3KTJTbtWsX9evX569//StLliyhcePGXkcSEREJK5WZKLZjxw4SExP5+uuvWb58OfXr1/c6koiISNipzESp7du3k5CQwA8//MCqVauoW7eu15FEREQ8oTIThb755hsSEhLYvXs3q1evpk6dOl5HEhER8YzKTJT58ssvSUhIYN++faxbt4477rjD60giIiKeUpmJIlu3biUxMZFDhw6RkZHBrbfe6nUkERERz6nMRIktW7aQmJhIbm4uGzZs4MYbb/Q6koiISETwxZfm+d0nn3xCvXr1AMjMzFSRERERyUdlJsJ99NFHxMfHU758ebKysqhVq5bXkURERCKKykwEe//990lMTOTss88mKyuLX/7yl15HEhERiTgqMxHqrbfeon79+px//vlkZ2dz9dVXex1JREQkIqnMRKCsrCwaNWrEpZdeSnZ2NtWrV/c6koiISMRSmYkw69ato3Hjxlx11VVkZWVxxRVXeB1JREQkovmizJhZiplNzMnJ8TrKGVm1ahXJyclcc801ZGZmctlll3kdSUREJOL5osw459Kcc12rVKnidZTTtmzZMpo1a0atWrXYsGEDF198sdeRREREooIvyky0W7hwIQ888AA33XQTGRkZXHjhhV5HEhERiRoqMx6bPXs2rVq14o477mDt2rWcf/75XkcSERGJKiozHpoxYwZt27alTp06pKenE82HyURERLyiMuORyZMn06lTJ+Lj41m5ciXnnHOO15FERESiksqMB8aOHUuXLl1o1KgRaWlpVK5c2etIIiIiUUtlJsxeeeUVevbsSUpKCkuWLKFSpUpeRxIREYlqKjNh9MILL9CnTx8eeOABFixYQIUKFbyOJCIiEvVUZsJk6NChDBgwgNTUVObMmUP58uW9jiQiIuILKjOlzDnHoEGDePrpp2nXrh0zZ86kbNmyXscSERHxDZWZUuSco3///jz77LN07tyZadOmERcX53UsERERX9EuglLinKN3796MHj2a7t278+qrr1KmjLqjiIhISdOnaynIy8ujR48ejB49ml69ejF27FgVGRERkVKiT9gSlpubS5cuXZgwYQL9+/dn5MiRmJnXsURERHxLZaYEHT9+nI4dOzJ16lQGDx7M8OHDVWRERERKmc6ZKSHHjh2jXbt2zJ07l6FDhzJo0CCvI4mIiMQElZkScPToUVJTU1m8eDEvvvgi/fr18zqSiIhIzFCZOUOHDx+mZcuWLF++nFGjRvHUU095HUlERCSmqMycgUOHDtGsWTPWrFnDuHHj6N69u9eRREREYo7KzGk6cOAAKSkpZGZmMnnyZDp37ux1JBERkZikMnMafvrpJ5o0acJbb73FjBkzaNeundeRREREYpbKTDHl5OTQuHFj3n//fWbNmkWrVq28jiQiIhLTVGaKYe/evTRq1IhNmzYxb948WrRo4XUkERGRmBexZcbMrgeeAi4CMpxz473Ms2fPHho0aMCWLVtYuHAhKSkpXsYRERGRoLB+A7CZTTWzXWb2WYHpSWa21cy2mdkAAOfc5865bsBDQO1w5ixo586dxMfH88UXX7B06VIVGRERkQgS7p8zmA4k5Z9gZnHAWKAxUAtobWa1gvPuB94EMsIb8z8OHTpEfHw8X375JcuXLycpKenUDxIREZGwCWuZcc5lA3sLTL4D2Oac+8o5dxSYAzQNLr/MOXc38HBR6zSzrma20cw27t69u8QzV6pUia5du5Kenk5iYmKJr19ERETOTCScM1MN+C7f/e3AnWZWD2gBVABWFvVg59xEYCJA7dq1XWkE7NWrV2msVkREREpAJJSZwn5W2jnnMoHM8EYRERGRaBPuc2YKsx24Mt/9K4AfPMoiIiIiUSYSyswHQE0zq2Fm5YFUYFlxVmBmKWY2MScnp1QCioiISOQK96XZs4F3gGvNbLuZdXbOHQd6AquBz4F5zrnNxVmvcy7NOde1SpUqJR9aREREIlpYz5lxzrUuYvpKTnKSr4iIiEhRIuEwk4iIiMhp80WZ0TkzIiIiscsXZUbnzIiIiMQuX5QZERERiV0qMyIiIhLVVGZEREQkqqnMiIiISFTzRZnR1UwiIiKxyxdlRlcziYiIxC5flBkRERGJXSozIiIiEtVUZkRERCSq+aLM6ARgERGR2OWLMqMTgEVERGKXL8qMiIiIxC5zznmdocSY2W7gm+DdKkBhx50Kmx7KtIuAPSUQMxRFZS+tx4ey/MmWieaxLuz5S/Px4RzrwqZrrIu3TDS/t7Ud0XvbD9uR6s65qqdcyjnnyxswMdTpoUwDNnqdvbQeH8ryJ1smmsc63OMdzrEuYmw11sVYJprf29qO6L1dEmNdxNh6OtaF3fx8mCmtGNNDnRYuZ/rcxX18KMufbJloHuuSeP7iPD6cY13YdI118ZaJ5ve2tiPhFU3v7WjfjvwXXx1mKk1mttE5V9vrHLFAYx0+Guvw0niHj8Y6fCJhrP28Z6akTfQ6QAzRWIePxjq8NN7ho7EOH8/HWntmREREJKppz4yIiIhENZUZERERiWoqMyIiIhLVVGZEREQkqqnMnAYzu97MJpjZAjPr7nWeWGBmlc3sQzNL9jqLn5lZPTN7I/j+rud1Hj8zszJm9qyZjTGzDl7n8Tszuyf4vp5sZm97ncfPzOwqM1tmZlPNbEA4nlNlJig46LvM7LMC05PMbKuZbTvxH8U597lzrhvwEKDvMTgNxRnvoP7AvPCm9IdijrUD9gMVge3hzhrtijnWTYFqwDE01qelmNvtN4Lb7eXADC/yRrNivrd/Caxwzj0C1ApHPpWZ/5gOJOWfYGZxwFigMYH/IK3NrFZw3v3Am0BGeGP6xnRCHG8zqw9sAXaGO6RPTCf09/YbzrnGBMrjkDDn9IPphD7W1wLvOOf6ANrDe3qmU4ztdlAbYHa4AvrIdEIf678AqWa2HtgQjnAqM0HOuWxgb4HJdwDbnHNfOeeOAnMI/N8Uzrllzrm7gYfDm9Qfijne8cBdBDZCXcxM79tiKM5YO+fygvP/BVQIY0xfKOb7ejuBcQbIDV9K/yjudtvMrgJynHP7wps0+hVzrDsBv3fOJQBNwpGvbDieJIpVA77Ld387cGfwXIIWBDb2Kz3I5VeFjrdzrieAmXUE9uT7wJXTV9R7uwXQCDgPeNWLYD5U6FgDo4ExZnYPkO1FMJ8qarwBOgPTwp7Iv4oa6wnAM2bWBvg6HEFUZk7OCpnmnHOZQGZ4o8SEQsf73/9wbnr4ovheUe/tRcCicIfxuaLG+iCBD1cpWUVuR5xzvw9zFr8r6r39GfBgOINod/3JbQeuzHf/CuAHj7LEAo13+Gisw0djHV4a7/CJmLFWmTm5D4CaZlbDzMoDqcAyjzP5mcY7fDTW4aOxDi+Nd/hEzFirzASZ2WzgHeBaM9tuZp2dc8eBnsBq4HNgnnNus5c5/ULjHT4a6/DRWIeXxjt8In2s9avZIiIiEtW0Z0ZERESimsqMiIiIRDWVGREREYlqKjMiIiIS1VRmREREJKqpzIiIiEhUU5kRiTFmVs/MnJld5HWWkmJmy81sutc5RMQbKjMiImFiZh3NbL/XOUT8RmVGREqdmZUxszivc4iIP6nMiPiMmVUws1FmttPMDpvZu2b2v4UsepeZfRxc5kMzuy3fOqqY2etmtis4/ysz61Vg/sTg/J/MLMvMaueb39HM9pvZfWb2GXAU6GlmR83swgJ5nzOzTfnu3x1c30Ez+97MxpvZufnmn2Vm04Pr32lmA0Mcl7vMbL2ZHTCzHDPLMLPLQxmzwg7NmdkvgtNqF1gm0czeC+bfaGa3npgPTAMqB5dzZvZMcF4LM/vEzA6Z2d7g678klNclIiozIn70ItAKeAS4BfgUSDezywosNwLoD9QGvgJWmNlZwXnDgBuAZOC64Lq+BzAzA1YA1YLzbwGygfUFnqMiMAh4DKgFTAf+CbQ8sUBwXa2BmcH7NwBrCPxY3U1AC+BmYGqB3A2AB4DE4PPfe7IBMbObgA3ANqAOcBcwDyhbzDELxXBgAHBr8PX+Ofg63wZ6AQeBy4K3EWZ2KTAHmAFcH3wtr5/G84rELuecbrrp5pMbUJnAXpD2+abFAV8Cw4L36wEOeDjfMmcDPwKPBu8vA6YV8RwJwH6gUoHpHwO/Df67Y/A5biuwzCvAG/nu/y+QC1QL3v8TMKXAY24OruviYM4jRWSffpJx+TPwbgmM2UX5lvlFcFrtAss0yrdMneC0K/KNy/4Cz39rcJnqXr9/dNMtWm/aMyPiL1cD5YC3TkxwzuUS+LXbWgWWfSffMvsJ7I04scx44CEz22RmI8ysbr7H3QacBewOHurZHzyp9dfB5z/hOIGCk99MoI6ZVQ/efxjIdM59n2/dbQus98RruTp4K19E9pO5BcgoYl5xxiwUn+T79w/BvxefZPlNwDrgMzNbaGbdzazqaTyvSMxSmRHxFwv+dYXMK2xaoZxzq4DqBA7pXETgENS04OwywE4Ce0zy364DBudbzZFgKci/3g+BL4A2jpR4jwAAAnlJREFUZlaOwCGnmfkWKQNMLrDem4CaBIqRcXpO9rhQxiyvkPWUK2J9xwp5fJHb2uAYNQzePgE6A38LHhoTkRCozIj4yzYCh0zyn7waB/wG2FJg2bvyLVOZwJ6Vz09Mc87tcc697pzrSOADtoOZVQA+Ai4B8pxz2wrcdoWQ8c8E9sgkETjEszDfvI+AXxWy3m3OuUPB13esiOwn8xGBw2OFCWXMdgf/5j+H5uZTPGdhjhI4hPUzLuAd59wQ4HYCe3Rancb6RWJS2VMvIiLRwjl3wMzGA8+b2R7g70BvAuVjXIHFB5nZbgIfnE8T+KCdBWBmfyBQADYT2E60AL5yzh0xs3UEDsksNbPfEtjTcimBcrLOOffGKWLOBIYGb8ucc/vyzXsBeNfMJgCvAT8R2OOT4px7zDm338ymAC8UyH6qy75fCq53IjAWOAzcA6xxzn0bwphtA74DnjGzAQTOlxl0iucszNdARTNrAPyFwMnANwL1gdUE9njdAlzJf5dPESmCyoyI//QP/p0GnEfgQzPJObejwHIDgJeBawmUlmTn3IHgvCPAs0ANAh/87wIpENiLYGb3EbjiaRKB80F2Eig4fzpVOOfcN2b2JoEyMbjAvE/M7N7gurMIlJSvgMX5Fvt/BPboLCZQBsYE75/sOT82s/rAc8HXcgTYSOCqLDjFmDnnjplZKoFys4nAIa+BwPJTvd4COd4OFrXZwIXAEGAugROFnwg+93fAUOfczCJXJCI/Y86FfBhdREREJOLonBkRERGJaiozIiIiEtVUZkRERCSqqcyIiIhIVFOZERERkaimMiMiIiJRTWVGREREoprKjIiIiES1/wN8Yxk4PpEX/AAAAABJRU5ErkJggg==\n", 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\n", 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    " ] From 255a413a9f03f4a410a9e8335a725dc28c3dd308 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Fri, 14 Jun 2019 11:09:03 -0400 Subject: [PATCH 19/26] Changed kepler example from count rates to counts --- observing_examples.ipynb | 74 ++++++++++++++++++++++------------------ 1 file changed, 40 insertions(+), 34 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 7098542..693ff2f 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -722,7 +722,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 10. Predicting count rates of the same stars, this time observed by a space-based telescope" + "## 10. Predicting photon counts of the same stars, this time observed by a space-based telescope" ] }, { @@ -767,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -776,31 +776,28 @@ " }\n", "\n", "for star in stars.keys():\n", - " stars[star]['observation'] = Observation(stars[star]['spectrum'], \n", - " kepler_response,\n", - " binset=kepler_response.waveset,\n", - " force='taper')" + " stars[star]['observation'] = Observation(stars[star]['spectrum'], kepler_response)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Compute the countrates for the two stars:" + "Compute the counts in a for the two stars:" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "synphot count rates:\n", - "\t HAT-P-11: 2.5 million e-/s\n", - "\t TRAPPIST-1 6.5 thousand e-/s\n" + "synphot counts in 10s exposure:\n", + "\t HAT-P-11: 24.7 million\n", + "\t TRAPPIST-1 65.0 thousand\n" ] } ], @@ -808,11 +805,12 @@ "kepler_area = np.pi * (1.4 * u.m / 2) ** 2 # area of Kepler's primary mirror\n", "\n", "for star in stars.keys():\n", - " stars[star]['countrate'] = stars[star]['observation'].countrate(area=kepler_area)\n", + " countrate = stars[star]['observation'].countrate(area=kepler_area)\n", + " stars[star]['counts'] = countrate * 10 * u.s\n", " \n", - "print('synphot count rates:')\n", - "print('\\t HAT-P-11:', round(stars['hatp11']['countrate'].value / 1e6, 1), 'million e-/s')\n", - "print('\\t TRAPPIST-1', round(stars['trappist1']['countrate'].value / 1e3, 1), 'thousand e-/s')" + "print('synphot counts in 10s exposure:')\n", + "print('\\t HAT-P-11:', round(stars['hatp11']['counts'].value / 1e6, 1), 'million')\n", + "print('\\t TRAPPIST-1', round(stars['trappist1']['counts'].value / 1e3, 1), 'thousand')" ] }, { @@ -832,19 +830,19 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "hatp11_r_apo = 34000000\n", - "hatp11_kepler = 2800000\n", + "hatp11_kepler = 28000000\n", "trappist1_z_apo = 203000\n", - "trappist1_kepler = 5700" + "trappist1_kepler = 57000" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -852,21 +850,21 @@ "output_type": "stream", "text": [ "Ground-based counts at Apache Point Observatory:\n", - "\t HAT-P-11 in r-band: 34 million counts\n", - "\t TRAPPIST-1 in z-band: 203 thousand counts\n", + "\t HAT-P-11 in r-band: 34 million\n", + "\t TRAPPIST-1 in z-band: 203 thousand\n", "Space-based count rates with Kepler:\n", - "\t HAT-P-11: 2.8 million e-/s\n", - "\t TRAPPIST-1 5.7 thousand e-/s\n" + "\t HAT-P-11: 28.0 million\n", + "\t TRAPPIST-1 57.0 thousand\n" ] } ], "source": [ "print('Ground-based counts at Apache Point Observatory:')\n", - "print('\\t HAT-P-11 in r-band:', int(hatp11_r_apo / 1e6), 'million counts')\n", - "print('\\t TRAPPIST-1 in z-band:', int(trappist1_z_apo / 1e3), 'thousand counts')\n", + "print('\\t HAT-P-11 in r-band:', int(hatp11_r_apo / 1e6), 'million')\n", + "print('\\t TRAPPIST-1 in z-band:', int(trappist1_z_apo / 1e3), 'thousand')\n", "print('Space-based count rates with Kepler:')\n", - "print('\\t HAT-P-11:', round(hatp11_kepler / 1e6, 1), 'million e-/s')\n", - "print('\\t TRAPPIST-1', round(trappist1_kepler / 1e3, 1), 'thousand e-/s')" + "print('\\t HAT-P-11:', round(hatp11_kepler / 1e6, 1), 'million')\n", + "print('\\t TRAPPIST-1', round(trappist1_kepler / 1e3, 1), 'thousand')" ] }, { @@ -878,22 +876,22 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 51, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
    " ] @@ -909,15 +907,16 @@ "\n", "plt.scatter(hatp11_r_apo, counts_hatp11,\n", " color='b', s=100, label=\"HAT-P-11, ground\")\n", - "plt.scatter(hatp11_kepler, stars['hatp11']['countrate'],\n", + "plt.scatter(hatp11_kepler, stars['hatp11']['counts'],\n", " color='b', marker= '^', s=100, label=\"HAT-P-11, space\")\n", "plt.scatter(trappist1_z_apo, counts_trappist1,\n", " color='r', s=100, label='TRAPPIST-1, ground')\n", - "plt.scatter(trappist1_kepler, stars['trappist1']['countrate'],\n", + "plt.scatter(trappist1_kepler, stars['trappist1']['counts'],\n", " color='r', marker= '^', s=100, label='TRAPPIST-1, space')\n", "\n", "# one-to-one line\n", - "plt.plot(np.linspace(1e3, 1e8, 20), np.linspace(1e3, 1e8, 20), color='black')\n", + "countrange = np.linspace(1e4, 1e8, 20)\n", + "plt.plot(countrange, countrange, color='black')\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", @@ -927,6 +926,13 @@ "\n", "plt.legend()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 186e9090817b83c6125b104b260e77590539b3fa Mon Sep 17 00:00:00 2001 From: tcjansen Date: Fri, 14 Jun 2019 13:27:23 -0400 Subject: [PATCH 20/26] Error when loading skycalc data on a different machine --- observing_examples.ipynb | 384 ++++++++++++++++----------------------- 1 file changed, 159 insertions(+), 225 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 693ff2f..0f3d4b1 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -61,21 +61,11 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created TAP+ (v1.0.1) - Connection:\n", - "\tHost: gea.esac.esa.int\n", - "\tUse HTTPS: False\n", - "\tPort: 80\n", - "\tSSL Port: 443\n" - ] - } - ], + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -111,9 +101,18 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 19, + "metadata": { + "collapsed": false + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query finished.\n" + ] + }, { "name": "stderr", "output_type": "stream", @@ -141,21 +140,7 @@ "WARNING: W06: None:177:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", "WARNING: W06: None:192:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", "WARNING: W06: None:204:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", - "WARNING" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query finished.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ": W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" ] @@ -191,8 +176,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 20, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "T_eff = round(T_eff, -2) # round to nearest 100 K\n", @@ -218,19 +205,19 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 21, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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J0yFQjPR5YjHenTgQXWOUkIniQSh3IjV8fqrCqhsZ42RQbpK74RvTISSUWLXOKs/lf/4E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\netv7VqnqN6p6EtaQFx/Y2zf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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -268,8 +255,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 22, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", @@ -294,21 +283,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -346,9 +334,35 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trying https (i.e. the actual link):\n", + "URLError: \n", + "Trying http instead:\n" + ] + }, + { + "ename": "ImportError", + "evalue": "cannot import name 'universaldetector'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mserver\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/observing/etc/api/skycalc'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m 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828\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCONTENT_CHUNK_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m//anaconda/lib/python3.5/site-packages/requests/models.py\u001b[0m in \u001b[0;36mapparent_encoding\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 694\u001b[0m \"\"\"Returns True if :attr:`status_code` is less than 400, False if not.\n\u001b[1;32m 695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m \u001b[0mThis\u001b[0m \u001b[0mattribute\u001b[0m \u001b[0mchecks\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0mcode\u001b[0m \u001b[0mof\u001b[0m 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\u001b[0mThis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m//anaconda/lib/python3.5/site-packages/requests/packages/chardet/__init__.py\u001b[0m in \u001b[0;36mdetect\u001b[0;34m(aBuf)\u001b[0m\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'universaldetector'" + ] + } + ], "source": [ "import json\n", "import requests\n", @@ -359,8 +373,24 @@ "paranal_id = '2640'\n", "\n", "# Use the default parameters from skycalc to set up the parameter dict\n", - "params_path = download_file('https://www.eso.org/observing/etc/' + \n", - " 'doc/skycalc/skycalc_defaults.txt')\n", + "params_path_https = 'https://www.eso.org/observing/etc/' + \\\n", + " 'doc/skycalc/skycalc_defaults.txt'\n", + "params_path_http = 'http://www.eso.org/observing/etc/' + \\\n", + " 'doc/skycalc/skycalc_defaults.txt'\n", + "\n", + "try:\n", + " print(\"Trying https (i.e. the actual link):\")\n", + " params_path = download_file(params_path_https)\n", + "except:\n", + " print('URLError: ')\n", + " try:\n", + " print(\"Trying http instead:\")\n", + " params_path = download_file(params_path_http)\n", + " except:\n", + " print(\"ImportError: cannot import name 'universaldetector'\")\n", + " \n", + "\n", "params = {}\n", "with open(params_path) as o:\n", " for row in o.readlines():\n", @@ -415,8 +445,10 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "with open('data.fits', 'wb') as f:\n", @@ -436,22 +468,11 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "quantum_efficiency = SpectralElement.from_file('ccd_QE.csv', wave_unit=\"nm\")\n", "quantum_efficiency = quantum_efficiency / 100 # convert percentages to decimals\n", @@ -516,22 +526,11 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "bp_atmos_qe = {} # setting up a dictionary for the different SDSS bands\n", "for band in sdss:\n", @@ -549,8 +548,10 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "band = 'rprime' # only want to know about the r' band\n", @@ -575,8 +576,10 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "countrate = observation.countrate(area=aperture_area) / gain" @@ -591,18 +594,11 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HAT-P-11 counts in the r-band, 10 s exposure:\n", - "38353638 cts\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "counts_hatp11 = countrate * 10 * u.s\n", "\n", @@ -629,8 +625,10 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", @@ -655,24 +653,12 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": { + "collapsed": false, "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "photlam_trappist1 = SourceSpectrum(Empirical1D,\n", " points=wavelengths, lookup_table=flux)\n", @@ -694,18 +680,11 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", - "306750 cts\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "band = 'zprime'\n", "\n", @@ -736,22 +715,11 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "kepler_response_path = download_file('https://keplergo.arc.nasa.gov/kepler_response_hires1.txt')\n", "kepler_response = SpectralElement.from_file(kepler_response_path, wave_unit=u.nm)\n", @@ -767,8 +735,10 @@ }, { "cell_type": "code", - "execution_count": 61, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", @@ -788,19 +758,11 @@ }, { "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "synphot counts in 10s exposure:\n", - "\t HAT-P-11: 24.7 million\n", - "\t TRAPPIST-1 65.0 thousand\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "kepler_area = np.pi * (1.4 * u.m / 2) ** 2 # area of Kepler's primary mirror\n", "\n", @@ -830,8 +792,10 @@ }, { "cell_type": "code", - "execution_count": 65, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "hatp11_r_apo = 34000000\n", @@ -842,22 +806,11 @@ }, { "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ground-based counts at Apache Point Observatory:\n", - "\t HAT-P-11 in r-band: 34 million\n", - "\t TRAPPIST-1 in z-band: 203 thousand\n", - "Space-based count rates with Kepler:\n", - "\t HAT-P-11: 28.0 million\n", - "\t TRAPPIST-1 57.0 thousand\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "print('Ground-based counts at Apache Point Observatory:')\n", "print('\\t HAT-P-11 in r-band:', int(hatp11_r_apo / 1e6), 'million')\n", @@ -876,32 +829,11 @@ }, { "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "fig = plt.figure(figsize=(9, 9))\n", "\n", @@ -930,7 +862,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] } @@ -938,7 +872,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -952,7 +886,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.5.2" } }, "nbformat": 4, From 69d4570497d0155d1b078ea621e757f8624f1588 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Fri, 14 Jun 2019 15:06:21 -0400 Subject: [PATCH 21/26] Skycalc error resolved - had to do with strict network firewall --- observing_examples.ipynb | 245 +++++++++++++++++++++++++-------------- 1 file changed, 161 insertions(+), 84 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 0f3d4b1..2a73521 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -70,6 +70,8 @@ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import requests\n", + "import json\n", "\n", "import astropy.units as u\n", "from astropy.io import fits\n", @@ -101,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -176,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -205,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 30, "metadata": { "collapsed": false }, @@ -255,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, "metadata": { "collapsed": true }, @@ -283,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 32, "metadata": { "collapsed": false, "scrolled": true @@ -334,62 +336,19 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 33, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Trying https (i.e. the actual link):\n", - "URLError: \n", - "Trying http instead:\n" - ] - }, - { - "ename": "ImportError", - "evalue": "cannot import name 'universaldetector'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0murl\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCONTENT_CHUNK_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m//anaconda/lib/python3.5/site-packages/requests/models.py\u001b[0m in \u001b[0;36mapparent_encoding\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 694\u001b[0m \"\"\"Returns True if :attr:`status_code` is less than 400, False if not.\n\u001b[1;32m 695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m \u001b[0mThis\u001b[0m \u001b[0mattribute\u001b[0m \u001b[0mchecks\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0mcode\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mbetween\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 697\u001b[0m \u001b[0;36m400\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;36m600\u001b[0m \u001b[0mto\u001b[0m \u001b[0msee\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthere\u001b[0m \u001b[0mwas\u001b[0m \u001b[0ma\u001b[0m \u001b[0mclient\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0ma\u001b[0m \u001b[0mserver\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mIf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 698\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0mcode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mbetween\u001b[0m \u001b[0;36m200\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mwill\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mThis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m//anaconda/lib/python3.5/site-packages/requests/packages/chardet/__init__.py\u001b[0m in \u001b[0;36mdetect\u001b[0;34m(aBuf)\u001b[0m\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'universaldetector'" - ] - } - ], + "outputs": [], "source": [ - "import json\n", - "import requests\n", - "from astropy.io import fits\n", - "\n", "# SkyCalc recognizes Cerro Paranal by its id number 2460, which we \n", "# got from the skycalc_cli source code\n", "paranal_id = '2640'\n", "\n", "# Use the default parameters from skycalc to set up the parameter dict\n", - "params_path_https = 'https://www.eso.org/observing/etc/' + \\\n", - " 'doc/skycalc/skycalc_defaults.txt'\n", - "params_path_http = 'http://www.eso.org/observing/etc/' + \\\n", - " 'doc/skycalc/skycalc_defaults.txt'\n", - "\n", - "try:\n", - " print(\"Trying https (i.e. the actual link):\")\n", - " params_path = download_file(params_path_https)\n", - "except:\n", - " print('URLError: ')\n", - " try:\n", - " print(\"Trying http instead:\")\n", - " params_path = download_file(params_path_http)\n", - " except:\n", - " print(\"ImportError: cannot import name 'universaldetector'\")\n", - " \n", + "params_path = download_file('http://www.eso.org/observing/etc/' + \\\n", + " 'doc/skycalc/skycalc_defaults.txt') \n", "\n", "params = {}\n", "with open(params_path) as o:\n", @@ -398,7 +357,7 @@ " columns = [column.strip() for column in columns]\n", " key, value = columns\n", " \n", - " # convert numbered values to floats\n", + " # convert numbered values to floats/ints\n", " try:\n", " value = int(value)\n", " except:\n", @@ -412,12 +371,13 @@ " if key == \"observatory\":\n", " params['observatory'] = paranal_id\n", " \n", - "# edit the parameters to your observation\n", + "# edit the parameters to match your observation\n", "params['airmass'] = 1.5\n", - " \n", + "\n", "# Use the bit from skycalc_cli which queries from the SkyCalc Sky Model\n", "server = 'http://etimecalret-001.eso.org'\n", "url = server + '/observing/etc/api/skycalc'\n", + "\n", "response = requests.post(url, data=json.dumps(params))\n", "results = json.loads(response.text)\n", "\n", @@ -445,9 +405,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -463,16 +423,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Then we combine with `synphot` so we can convolve the atmospheric effects with other parts of the observation:" + "### Then we combine with `synphot` so we can convolve the atmospheric effects with other parts of the observation:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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F5t1w6bg+XofgGS2VKS8kWn30ANYKabUAxpj5WBPcZa33V25lcNdSykqSq/8N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\nvtbKYTn16dm2z0oKnTQpqCx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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "quantum_efficiency = SpectralElement.from_file('ccd_QE.csv', wave_unit=\"nm\")\n", "quantum_efficiency = quantum_efficiency / 100 # convert percentages to decimals\n", @@ -526,11 +508,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Y/nzZ/o56/MLP08DS8G/hEWBZ2d/KQX8j9bbPoXwRyWM3fgnMPZLHOPM//IwP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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "bp_atmos_qe = {} # setting up a dictionary for the different SDSS bands\n", "for band in sdss:\n", @@ -548,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -576,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -594,11 +587,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HAT-P-11 counts in the r-band, 10 s exposure:\n", + "38353638 cts\n" + ] + } + ], "source": [ "counts_hatp11 = countrate * 10 * u.s\n", "\n", @@ -625,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -653,12 +655,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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YsGSL7+esbLILenLO6wYcG+j5TcWS5VkT4x3PxsSKSBowEjgfyAFmi8hYVV0S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\nqo4F3gDecQcP7MRJJLj1xuA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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "photlam_trappist1 = SourceSpectrum(Empirical1D,\n", " points=wavelengths, lookup_table=flux)\n", @@ -680,11 +693,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAPPIST-1 counts in the z-band, 10 s exposure:\n", + "306750 cts\n" + ] + } + ], "source": [ "band = 'zprime'\n", "\n", @@ -715,11 +737,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "kepler_response_path = download_file('https://keplergo.arc.nasa.gov/kepler_response_hires1.txt')\n", "kepler_response = SpectralElement.from_file(kepler_response_path, wave_unit=u.nm)\n", @@ -735,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -758,11 +791,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "synphot counts in 10s exposure:\n", + "\t HAT-P-11: 24.7 million\n", + "\t TRAPPIST-1 65.0 thousand\n" + ] + } + ], "source": [ "kepler_area = np.pi * (1.4 * u.m / 2) ** 2 # area of Kepler's primary mirror\n", "\n", @@ -792,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -806,11 +849,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground-based counts at Apache Point Observatory:\n", + "\t HAT-P-11 in r-band: 34 million\n", + "\t TRAPPIST-1 in z-band: 203 thousand\n", + "Space-based count rates with Kepler:\n", + "\t HAT-P-11: 28.0 million\n", + "\t TRAPPIST-1 57.0 thousand\n" + ] + } + ], "source": [ "print('Ground-based counts at Apache Point Observatory:')\n", "print('\\t HAT-P-11 in r-band:', int(hatp11_r_apo / 1e6), 'million')\n", @@ -829,11 +885,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KgFdeeYXXXnuNSpUqJRcXgE6dOlGiRAl8fHyoWLEiEyZMwMvLi6lTp/Lqq69S\nsWJFfH19+fnnn2/0RyMikqksWLCA+vXrU7p0aaKjo7n77rudjpSpmZT+J5RV+fn52ZUrV14ytnHj\nRsqVK3f1B3brBl99lXqZAfDygk6d3LJ2JqubPXs227dvp1evXi4dX6tWLQYPHuz4tRFc+t0QEUmj\nbdtgyBAYPx5OnIACBaB1a3jppcTrs15uzpw5NG3alAcffJCFCxe6bV1jVmCMWWWtveb/LDQzAzBz\n5tWLDCTeP2NGxuTJYkJDQ10uMiIi2UlEBPj4wJgxcPw4WJv4dcyYxPGIiEuPnz59Oo0bN8bb25vI\nyMhsXWSuh85mAti92+kE2crFZ1iJiHiqbdugWTO4sFTxEnFxiX+aNYM1axJnaCZPnkyrVq2oUqUK\nERER3HLLLRkfOovyiDJjjAkDwu67774U77fWXnH2jGRvnvTxqohkTkOGJBaWq4mLg2HDoFq172nb\nti2PPfYYc+fO5eabb86YkB7C49fM7Nixg5tvvpkiRYqo0AiQWGQOHz7M8ePHdZqjiLhNwYKJHyld\nS968X3P2bEdq1arFrFmzyJ8/v/vDZRGurpnxiJmZqylevDi7d+/m4MGDTkeRTCRv3ryXbO0gIpLe\nTpxw5agvOHOmK8HBwUyfPp2bbrrJ3bE8kseXmdy5c+tv3yIikuEKFLjWzMwIoBe5cjVgxoyp5M2b\nN4OSeR6dzSQiIuIGrVtD7typ3TsE6IUxjejUaZqKTBqpzIiIiLjBSy+lVmbeA/oAT5I372T69PHK\n2GAeSGVGRETEDcqUgalT4aabkkqNBfoDr2NMa/Ll+54ffsid4oXz5PqozIiIiLhJvXqJ15Hp3Nni\n5dUPeIfcuZ+ha9dxrF2bi3r1nE7oGTx+AbCIiIiT7r3XkjdvH86dG0qXLl349NNPyZFDcwnpST9N\nERERN0lISKBXr14MHTqUnj178tlnn6nIuIF+oiIiIm6QkJBA165dGTlyJH369OGTTz7RxVvdRGVG\nREQkncXHx9OxY0e+/PJL+vXrx//+9z8VGTfSmhkREZF0dP78edq1a8eECRPo378/b731loqMm6nM\niIiIpJO4uDiefvpppkyZwvvvv0/fvn2djpQteMTHTMaYMGPM6KNHjzodRUREsqmzZ8/SvHlzpkyZ\nwtChQ1VkMpBHlBlr7Sxr7bOFChVyOoqIiGRDZ86coUmTJsyYMYORI0fSu3dvpyNlK/qYSUREJA1O\nnTrFE088weLFi/niiy949tlnnY6U7ajMiIiI3KATJ04QFhZGdHQ0Y8eOpX379k5HypZUZkRERG7A\nsWPHqF+/Pr/++ivjx4+nVavn0/wyAAAgAElEQVRWTkfKtjxizYyIiEhG+u+//wgODua3337js88m\n8uabrdi/3+lU2ZfKjIiIyHU4fPgwgYGB/P7770ydOpXVq5uxcycMHOh0suxLZUZERMRF//zzDwEB\nAaxfv54ff/yRqlWf4OuvISEBvv4azc44RGVGRETEBfv376d27dps2bKFWbNmUb9+fQYOTCwyAPHx\nmp1xisqMiIjINezZswd/f3/++usv5s6dS1BQEPv2Jc7GnDuXeMy5c5qdcYrKjIiIyFX8/fff+Pv7\ns2/fPubPn0+tWrUALpmVSaLZGWeozIiIiKRi+/bt1KxZk0OHDrFw4UKqV68OcMWsTBLNzjhDZUZE\nRCQFW7Zswd/fn+PHjxMZGckjjzySfF9KszJJNDuT8VRmRERELrNx40b8/f05e/YsS5YsoXLlysn3\npTYrk0SzMxlPZUZEROQi69ato1atWlhriYqKwsfH55L7rzYrk0SzMxlLZUZEROSC1atXU6tWLXLn\nzk10dDTly5e/4piZM1OflUly7hzMmOGmkHIF7c0kIiICrFixguDgYAoWLEhkZCRlypRJ8bjduzM4\nmFyTZmZERCTb++WXX6hTpw633norMTExqRYZyZxUZkREJFuLiYkhODiYO++8k5iYGEqWLOl0JLlO\nKjMiIpJtLV68mHr16nHPPfcQHR1N8eLFnY4kN8AjyowxJswYM/ro0aNORxERkSxi/vz5hIaGUqZM\nGaKioihatKjTkeQGeUSZsdbOstY+W6hQIaejiIhIFjB79mwaNmzIgw8+SGRkJHfccYfTkSQNPKLM\niIiIuGr69Ok0adKEihUrEhkZyW233eZ0JEkjlRkREck2Jk2aRPPmzfHz82PhwoXceuutTkeSdKAy\nIyIi2cJ3331Hq1atqF69OvPnz0dLEzyHyoyIiHi8sWPH0q5dO2rVqsXcuXO5+eabnY4k6UhlRkRE\nPNrnn39Ox44dCQ4OZvbs2eTPn9/pSJLOVGZERMRjDR8+nOeee47Q0FB+/PFH8uXL53QkcQOVGRER\n8UgfffQRzz//PE2aNOGHH34gb968TkcSN1GZERERj/Puu+/yyiuv8NRTTzFx4kS8vLycjiRupDIj\nIiIew1rLW2+9xZtvvkmbNm0YP348uXPndjqWuFkupwOIiIikB2str732Gh9++CEdOnRg9OjR5MyZ\n0+lYkgFUZkREJMuz1vLiiy/y8ccf89xzzzFy5Ehy5NCHD9mF/kmLiEiWlpCQQI8ePfj44495/vnn\nGTVqlIpMNqN/2iIikmUlJCTQpUsXPv30U15++WWGDRuGMcbpWJLBVGZERCRLio+P55lnnmHMmDG8\n8cYbfPjhhyoy2ZTWzIiISJZz/vx52rZtS3h4OAMGDODNN990OpI4SGVGRESylHPnztGqVSt++OEH\nPvzwQ1555RWnI4nDVGZERCTLOHv2LM2bN2fWrFkMGzaMF154welIkgmozIiISJZw+vRpmjRpwrx5\n8xg1ahTdunVzOpJkEiozIiKS6Z06dYqGDRsSGRnJl19+SadOnZyOJJmIyoyIiGRqJ06cIDQ0lKVL\nlzJu3Djatm3rdCTJZFRmREQk0zp69Cj169fnt99+4/vvv6dFixZOR5JMSGVGREQypX///ZeQkBBW\nr17NpEmTaNq0qdORJJNSmRERkUzn0KFDBAcHs379eqZNm0ZYWJjTkSQTU5kREZFM5Z9//qFOnTr8\n+eefzJgxg7p16zodSTI5lRkREck09u3bR2BgIDt37mTOnDkEBgY6HUmyAJUZERHJFHbv3k1AQAB7\n9+4lIiICf39/pyNJFqEyIyIijvvrr78ICAjg4MGDLFiwgMcee8zpSJKFqMyIiIijtm3bRkBAAMeO\nHWPRokVUrVrV6UiSxWTaMmOMqQE8TWLG8tZa1XQREQ/z559/EhAQwOnTp1m8eDGVK1d2OpJkQTky\n8sWMMWONMf8YY9ZdNl7XGLPZGLPVGNMXwFq71FrbFZgNfJOROUVExP02bNiAv78/586dY8mSJSoy\ncsMytMwA44BLzrEzxuQERgH1gPJAS2NM+YsOaQVMyKiAIiLifmvWrKFWrVoAREVF4ePj42wgydIy\ntMxYa2OAI5cNVwW2Wmu3W2vPAROBJwCMMSWAo9ba46k9pzHmWWPMSmPMyoMHD7oruoiIpJPff/+d\n2rVr4+XlRXR0NOXLl7/2g0SuIqNnZlJSDNh10e3dF8YAOgJfX+3B1trR1lo/a63f7bff7qaIIiKS\nHpYvX05gYCAFChQgOjqaBx54wOlI4gEy7QJgAGvt205nEBGR9PHTTz9Rr149br/9diIjIylZsqTT\nkcRDZIaZmT3APRfdLn5hTEREPER0dDQhISEULVqU6OhoFRlJV5mhzKwA7jfGlDbGeAEtgJkOZxIR\nkXSyaNEi6tWrR8mSJYmKiqJ48eJORxIPk9GnZocDvwBljTG7jTEdrbXngR7AfGAjMNlau/46nzfM\nGDP66NGj6R9aRERuWEREBKGhodx3330sWbKEokWLOh1JPJCx1jqdId34+fnZlStXOh1DRESAmTNn\n0rx5cypUqMDChQspUqSI05EkizHGrLLW+l3ruMzwMZOIiHiYH374gaZNm+Lr68vixYtVZMStVGZE\nRCRdhYeH89RTT1G1alUWLlzIrbfe6nQk8XAqMyIikm6++eYbWrduzeOPP878+fMpWLCg05EkG1CZ\nERGRdDFmzBieeeYZAgICmDt3LgUKFHA6kmQTHlFmdDaTiIizPv30Uzp37kxISAgzZ87kpptucjqS\nZCMeUWastbOstc8WKlTI6SgiItnOxx9/TPfu3WnYsCE//vgj+fLlczqSZDMeUWZERMQZH374Ib17\n96Zp06ZMmTKFPHnyOB1JsiGVGRERuSEDBw6kb9++tGzZkokTJ+Ll5eV0JMmmVGZEROS6WGt54403\neOutt2jbti3fffcduXJl6n2LxcPpt09ERFxmreXVV1/lo48+olOnTnzxxRfkyKG/F4uzPOI3UGcz\niYi4n7WW3r1789FHH9GtWzcVGck0POK3UGcziYi4V0JCAt26deOTTz6hd+/ejBw5UkVGMg39JoqI\nyFXFx8fTuXNnPv/8c1599VWGDBmCMcbpWCLJVGZERCRV58+fp3379owdO5a33nqL999/X0VGMh0t\nABYRkRTFxcXRpk0bJk2axLvvvsvrr7/udCSRFKnMiIjIFc6dO0eLFi2YPn06H330EX369HE6kkiq\nVGZEROQSZ86coXnz5syePZtPPvmEXr16OR1J5KpUZkREJNnp06dp1KgRCxYs4LPPPqNr165ORxK5\nJo9YAKzrzIiIpN3Jkydp0KABCxcu5KuvvlKRkSzDI8qMrjMjIpI2x48fp169ekRHR/Ptt9/SoUMH\npyOJuEwfM4mIZHNHjx6lXr16LF++nAkTJvDUU085HUnkuqjMiIhkY0eOHCEkJIQ//viDKVOm0Lhx\nY6cjiVw3lRkRkWzq0KFDBAUFsWHDBqZNm0ZoaKjTkURuiMqMiEg2dODAAerUqcPWrVuZOXMmISEh\nTkcSuWEqMyIi2czevXsJDAzk77//Zs6cOQQEBDgdSSRNVGZERLKRXbt2ERAQwP79+5k3bx41atRw\nOpJImqnMiIhkEzt37iQgIIDDhw+zYMECqlWr5nQkkXThEdeZ0UXzRESubuvWrdSsWZN///2XxYsX\nq8iIR/GIMqOL5omIpG7z5s34+/tz6tQplixZgp+fn9ORRNKVPmYSEfFg69evJzAwEGstUVFReHt7\nOx1JJN15xMyMiIhc6Y8//qBWrVrkyJGD6OhoFRnxWCozIiIeaNWqVdSuXZu8efMSHR3Ngw8+6HQk\nEbdRmRER8TC//vorgYGBFCxYkJiYGO6//36nI4m4lcqMiIgHWbZsGcHBwdx2223ExMRQunRppyOJ\nuJ3KjIiIh4iKiqJu3brcfffdREdHU6JECacjiWQIlRkREQ+wcOFC6tevT8mSJYmKiqJYsWJORxLJ\nMCozIiJZ3Ny5cwkLC+P+++8nKiqKu+66y+lIIhnKI8qMrgAsItnVjBkzaNSoERUqVCAyMpLbb7/d\n6UgiGc4jyoyuACwi2dGUKVNo1qwZlStXZvHixRQpUsTpSCKO8IgyIyKS3UyYMIEWLVrw6KOPsmDB\nAm655RanI4k4RmVGRCSLGTduHK1bt6ZmzZpERERQsGBBpyOJOEplRkQkCxk9ejTPPPMMderUYc6c\nORQoUMDpSCKOU5kREckiRo4cSZcuXahfvz4zZ87kpptucjqSSKagMiMikgUMHTqUnj178sQTTzBt\n2jTy5s3rdCSRTENlRkQkk3v//fd56aWXaN68OVOmTCFPnjxORxLJVFRmREQyKWst77zzDv369aNV\nq1ZMmDCB3LlzOx1LJNPJ5XQAERG5krWWN954g/fee4/27dszZswYcubM6XQskUxJZUZEJJOx1vLy\nyy8zZMgQnn32WT777DNy5NBEukhqVGZERDIRay3PP/88I0aMoEePHgwfPhxjjNOxRDI1VX0RkUwi\nISGBrl27MmLECF588UUVGREXqcyIiGQC8fHxdOzYkdGjR/Paa68xePBgFRkRF3lEmdGu2SKSlZ0/\nf562bdsybtw4+vfvz6BBg1RkRK6DR5QZ7ZotIllVXFxc8mnX7733Hm+//baKjMh1cqnMGGOeNMYE\nX3T7LWPMbmPMfGNMUffFExHxXGfPnuXJJ59kypQpDBkyhNdee83pSCJZkqszM/2TvjHGVAb6AcOB\n3MCQ9I8lIuLZzpw5Q5MmTfjxxx+TF/yKyI1x9dTsksDmC983Bn601v7PGLMAmO+WZCIiHurUqVM0\natSIhQsX8sUXX/Dss886HUkkS3N1ZuYMcPOF7wOBRRe+P3rRuIiIXMPJkycJDQ1l0aJFjB07VkVG\nJB24OjOzFBhijFkG+AHNLow/AOxyRzAREU9z7NgxGjRowM8//8x3333H008/7XQkEY/g6sxMD+Ac\niSWmq7V274XxeuhjJhGRa/rvv/8IDg7ml19+YeLEiSoyIunIpZkZa+1uICyF8RfSPZGIiIc5cuQI\nwcHBrFmzhqlTp9KoUSOnI4l4FFdPzd5ujCmSwvgtxpjt6R9LRMQzHDx4kICAANatW8f06dNVZETc\nwNU1M6WAlPaezwMUT7c0IiIeZP/+/dSpU4dt27Yxc+ZMgoODr/0gEbluVy0zxpgmF91sYIy5eL+A\nnCSe2bTDHcFERLKyPXv2EBgYyK5du5g7dy61a9d2OpKIx7rWzMzUC18t8NVl98UBO4GX0jmTiEiW\n9vfffxMQEMCBAweYP38+jz/+uNORRDzaVcuMtTYHgDFmB1DFWnsoQ1KJiGRRO3bsICAggH///ZeF\nCxfy6KOPOh1JxOO5ejZTaXcHERHJ6rZu3Urt2rU5efIkixcv5uGHH3Y6kki24OoCYIwxj5C4RuYO\nLjsLylrbK51ziYhkKZs2bSIgIIC4uDiWLFlCxYoVnY4kkm24VGaMMX2A/wFbgb0krqFJYlN8kIhI\nNrFu3ToCAwMxxhAVFUWFChWcjiSSrbg6M/M80MtaO9KdYUREsprY2Fjq1KmDl5cXkZGRPPjgg05H\nEsl2XN3OoCAw151BRESympUrVxIQEMBNN91ETEyMioyIQ1wtM+FAXXcGERHJSn799VcCAwMpVKgQ\nMTEx3HfffU5HEsm2XP2YaRfwjjGmOrCGxGvMJLPWDk3vYNfDGBMGhOk/JiKSEZYuXUr9+vW56667\niIyM5J577nE6kki2Zqy99vrdC9eZSY211t6bfpFunJ+fn125cqXTMUTEg0VGRhIWFsY999xDZGQk\nd999t9ORRDyWMWaVtdbvWsfpOjMiIi6aP38+jRo1okyZMixevJg777zT6UgigutrZkREsrXZs2fT\nsGFDypYty5IlS1RkRDIRV68zM/xq9+uieSLiyaZPn85TTz1FxYoVmT9/PoULF3Y6kohcxNUFwA9d\ndjs38CCJO2evTtdEIiKZyOTJk2nVqhVVqlRh3rx5FCpUyOlIInIZV9fMXLF3vTEmL4k7aS9N71Ai\nIpnB+PHjadeuHY899hhz587l5ptvdjqSiKTghtfMWGvPAO8Br6dfHBGRzOHrr7+mbdu2+Pv7M2/e\nPBUZkUwsrQuAbwMKpEcQEZHM4osvvqBDhw4EBQUxe/Zs8ufP73QkEbkKVxcAv3j5EFAUeBptcyAi\nHmTEiBH06tWLBg0aMHXqVPLmzet0JBG5BlcXAPe87HYCcBD4Gng/XROJiDhk8ODBvPzyyzRu3JiJ\nEyfi5eXldCQRcYEumiciAgwaNIg33niDJ598kvHjx5M7d26nI4mIi657zYwxpoAxRh8gi4hHsNby\n9ttv88Ybb9C6dWu+//57FRmRLMblMmOM6W6M+Rs4ChwzxvxljOnmvmgiIu5lraVfv34MGDCAZ555\nhnHjxpErl6ufvotIZuHqAuB+wGvAYGDZheEawAfGmILW2g/clE9ExC2stbz00ksMGzaMLl268Omn\nn5Ijh3Z4EcmKXP0rSFfgWWtt+EVji40xW0i81ozKjIhkGQkJCfTq1YtRo0bRs2dPPvnkE4wxTscS\nkRvk6l9D7gBWpDC+HNBuayKSZSQkJNC1a1dGjRpFnz59VGREPICrZeZPoFUK462AzekXR0TEfeLj\n4+nQoQNffvklr7/+Ov/73/9UZEQ8gKsfM/UHJhtjagI/XRirDvgDzd2QS0QkXZ0/f5527doxYcIE\nBgwYwJtvvul0JBFJJ65eZ2aaMeYRoDcQemF4I1DVWqtds0UkU4uLi6NVq1ZMnTqV999/n759+zod\nSUTSkcvnIFprVwGt3ZhFRCTdnT17lieffJKZM2cydOhQevfu7XQkEUlnrp6a3Rw4Z62dcdn4E0Bu\na+1Ud4QTEUmLM2fO0KRJEyIiIhg5ciTdu3d3OpKIuIGrC4D7A2dSGD954T4RkUzl1KlThIWFMW/e\nPEaPHq0iI+LBXP2Y6V5SPmtp64X7REQyjRMnThAWFkZ0dDRff/017dq1czqSiLiRqzMz/wL3pzD+\nAHA8/eKIiKTNsWPHqFu3LkuXLmX8+PEqMiLZgKtlZgYwzBjzQNKAMaYsMBT40R3BRESu17///ktQ\nUBC//fYbEydOpFWrlC6PJSKextUy8yqJG0xuMMbsMsbsAtYDx4CX3RVORMRVhw8fpk6dOqxevZqp\nU6fSrFkzpyOJSAZx9Tozx4DqxpggwPfC8GpgsbXWuiuciIgr/vnnH4KCgti8eTMzZsygXr16TkcS\nkQx0XXvdW2sXAgvdlEVE5Lrt27ePOnXqsGPHDmbPnk2dOnWcjiQiGey6yoyISGayZ88eAgIC2LNn\nD3PnzqVWrVpORxIRB2TaMmOMyQEMBAoCK6213zgcSUQykb/++ouAgAAOHjzI/PnzqV69utORRMQh\nri4AThfGmLHGmH+MMesuG69rjNlsjNlqjEnaNOUJoDgQB+zOyJwikrlt374df39/Dh8+zMKFC1Vk\nRLK5DC0zwDig7sUDxpicwCigHlAeaGmMKQ+UBX621r4IPJfBOUUkk9qyZQv+/v4cP36cyMhIHnnk\nEacjiYjDXCozxpjtxpgiKYzfYozZ7uqLWWtjgCOXDVcFtlprt1trzwETSZyV2U3ixfoA4q+S7Vlj\nzEpjzMqDBw+6GkVEsqCNGzdSs2ZNzpw5Q2RkJJUrV3Y6kohkAq7OzJQCcqYwngcolsYMxYBdF93e\nfWFsGhBijBkBxKT2YGvtaGutn7XW7/bbb09jFBHJrNauXYu/vz/WWqKioqhYsaLTkUQkk7jqAmBj\nTJOLbjYwxhy96HZOIBDY6YZcWGtPAR3d8dwikrWsXr2aoKAg8uTJQ2RkJGXLlnU6kohkItc6m2nq\nha8W+Oqy++JILDIvpTHDHuCei24XvzAmIsKKFSsIDg6mYMGCREZGUqZMGacjiUgmc9UyY63NAWCM\n2QFUsdYeckOGFcD9xpjSJJaYFoA2VBERfv75Z+rVq0eRIkWIjIykVKlSTkcSkUzIpTUz1trS6VFk\njDHhwC9AWWPMbmNMR2vteaAHMB/YCEy21q6/zucNM8aMPnr06LUPFpEsISYmhuDgYO68806io6NV\nZEQkVcbVrZWMMQ1I3HCyPIkfO20APrTWznVfvOvj5+dnV65c6XQMEUmjxYsXExYWRsmSJYmMjKRo\n0aJORxIRBxhjVllr/a51nKunZncCpgPbSCw0fYEdwHRjTIe0BBURudj8+fMJDQ2lTJkyREVFqciI\nyDW5up3Bq8CL1tqRF419ZYxZRWKxGZvuyUQk25k1axbNmjWjfPnyLFy4kNtuu83pSCKSBbh6nZkS\nwLwUxiOAkukXR0Syq2nTptGkSRN8fHxYvHixioyIuMzVMvM3EJTCeDDwV/rFEZHsaOLEiTz55JNU\nqVKFRYsWUbhwYacjiUgW4urHTIOBEcaYysDPF8aqA22Anu4Idj2MMWFA2H333ed0FBG5Tt999x3t\n27fn8ccfZ/bs2dx8881ORxKRLMbVU7O/AJ4CypFYbAYDDwJPWmtHuy+ea6y1s6y1zxYqVMjpKCJy\nHcaOHUu7du2oVasWc+fOVZERkRvi6swM1trpJJ7RJCKSZp999hndunUjJCSE6dOnky9fPqcjiUgW\n5XKZSWKMuYXLZnSstZfvhC0ikqpPPvmEF154gdDQUKZMmULevHmdjiQiWZir15kpaYyJMMacBg4D\nBy/8OXThq4iISz766CNeeOEFmjRpwg8//KAiIyJp5urMzNfALSTuYr2XxCsAi4hcl3fffZc333yT\nFi1a8O2335I7d26nI4mIB3C1zFQFHrXWrnNnGBHxTNZa3n77bQYOHEibNm0YO3YsuXJd96fcIiIp\ncvU6MzuAPO4MkhbaaFIk87LW0rdvXwYOHEiHDh34+uuvVWREJF25WmaeB943xmTKC7no1GyRzMla\nS+/evfnf//7Hc889x5dffknOnDmdjiUiHibVvx4ZY45z6dqYvMBmY8xZ4PzFx1prC7onnohkVQkJ\nCfTo0YPPPvuM559/nmHDhmGMcTqWiHigq8319siwFCLiURISEujSpQtjxozhlVde4YMPPlCRERG3\nSbXMWGu/ycggIuIZ4uPj6dChA99++y1vvPEGAwYMUJEREbdyeRWeMSYv0Aoof2FoAxBurT3tjmAi\nkvXExcXRtm1bJk6cyIABA3jzzTedjiQi2YBLZebCBpOzgXzA2gvDHYBBxpgG1trf3ZRPRLKIc+fO\n0bJlS6ZNm8aHH37IK6+84nQkEckmXD2baTSwDChura1pra0J3APEXLjPUTo1W8RZZ8+epVmzZkyb\nNo1hw4apyIhIhnK1zFQA+ltrTyYNXPh+wIX7HKVTs0Wcc/r0aRo1asSsWbMYNWoUL7zwgtORRCSb\ncbXMbALuTmG8KPBn+sURkazk5MmThIWFMX/+fL788ku6devmdCQRyYZcXQD8BjDcGDMA+PXC2KMX\nxvsaYwonHagdtEWyh+PHjxMaGsqyZcsYN24cbdu2dTqSiGRTrpaZWRe+TuD/L6SXdK7ljItuW0CX\n9xTxcEePHqVevXosX76c77//nhYtWjgdSUSyMVfLTG23phCRLOPff/8lJCSE1atXM2nSJJo2bep0\nJBHJ5lwqM9baaHcHEZHM79ChQwQHB7N+/XqmTZtGWFiY05FERK7ronk3Ab7AHVy2cNhaOy2dc4lI\nJvPPP/9Qp04d/vzzT2bMmEHdunWdjiQiArh+0bw6QDhQJIW7tU5GxMPt27ePwMBAdu7cyZw5cwgM\nDHQ6kohIMldPzf4EmEPiRfNyXPbH8SKji+aJuM/u3bvx9/fn77//JiIiQkVGRDIdV8tMKWCgtXav\nG7PcMF00T8Q9/vrrL/z9/dm/fz8LFizA39/f6UgiIldwtcz8BJR1ZxARyVy2bdtGzZo1OXLkCIsW\nLeKxxx5zOpKISIpcXQD8OTDYGHM3iRtNxl18pzaaFPEsmzdvJjAwkNOnT7N48WIqV67sdCQRkVS5\nWmamXvia0qaSWgAs4kE2bNhAYGAg8fHxREVF8dBDDzkdSUTkqlwtM6XdmkJEMoU1a9ZQp04dcubM\nSVRUFOXLl3c6kojINblaZk5Zaw+6NYmIOOr3338nKCiIfPnyERkZyQMPPOB0JBERl7i6AHiPMeYH\nY0w9Y4y59uEikpUsX76cwMBAChQoQHR0tIqMiGQprpaZBsBZ4Afgb2PMQGNMGffFEpGM8tNPP1Gn\nTh0KFy5MTEwMZcroX20RyVpcKjPW2oXW2lbA3cAHQD3gT2NMpDHmaWNMXneGFBH3iI6OJiQkhKJF\nixIdHU3JkiWdjiQict1cnZkBwFr7n7V2lLXWD+gFPAZ8B+w1xnxgjCngjpAikv4WLVpEvXr1KFGi\nBFFRURQvXtzpSCIiN+S6yowxpqgxpq8xZhPwITAR8AeeA+oCP6Z/RJdyaTsDkesQERFBaGgo9913\nH1FRURQtWtTpSCIiN8xYa699kDFNgA5AMLAOGAN8b609etEx9wKbrLVebsp6TX5+fnblypVOvbxI\nljBz5kyaN29OhQoVWLhwIUWKpLR/rIiI84wxqy58GnRVrp6a/TWJu2ZXs9auSuWYfcAgF59PRBzw\nww8/0KJFCypXrsy8efO49dZbnY4kIpJmrpaZotbaU1c7wFp7Gngn7ZFExB3Cw8Np06YNjzzyCBER\nERQsWNDpSCIi6cLlXbONMckbTRpjgowx440xrxljtJWBSCb3zTff0Lp1a6pXr878+fNVZETEo7ha\nZsYClQCMMfcAM4DCQHfgXfdEE5H0MGbMGJ555hlq167N3LlzKVBAJx2KiGdxtcw8CCTtjN0M+M1a\nWx9oA7R0RzARSbtRo0bRuXNnQkJCmDVrFvnz53c6kohIunO1zOQEzl34PhCYe+H7bcCd6R1KxGNt\n2wbdukHBgpAjR+LXbrBPZx4AACAASURBVN0Sx9PZsGHD6NGjBw0bNuTHH38kX7586f4aIiKZgatl\nZh3wnDGmBollZt6F8WLAIXcEE/E4ERHg4wNjxsDx42Bt4tcxYxLHIyLS7aU+/PBDXnzxRZo2bcqU\nKVPIkydPuj23iEhm42qZeRXoDEQB4dbatRfGGwLL3ZBLxLNs2wbNmsGpUxAXd+l9cXGJ482apcsM\nzcD/a+++w6Mq8/eP3x+aCCoqslhRLnR1sS6irstXSCZAQgmwIAJSpAgiNpQfwvJFXEVXZZFVihSR\nopEmICXSSxLrIhZQVFbsYAEsSE8gz++PGb5XjJBGMs+U9+u65gpzzjNn7pkHMjfnzJkZPlyDBw9W\nx44dNWvWLFWq5O2jnwAgLIr63UxZkmpIOsM51zPPqokKfvovgII8+eTvS0x+OTnSv/9d4rtwzmno\n0KEaNmyYunXrprS0NFWoUNRPXwCA6FXkrzNwzh12zv2cb9mXzrntpR8LiDFpaUUrMy+8UKLNO+d0\n//3369FHH9Wtt96qqVOnqnx5PjUBQHwo1nczASihPXtKd1wezjn1799fI0eOVL9+/TRx4kSVK8c/\nbQDxg994QDgU9bNdivkZMLm5uerXr59Gjx6t/v37a+zYsRQZAHEnJn7r8a3ZiHhdukgVKxY8pmJF\nqWvXIm/y8OHD6t27tyZMmKBBgwZp1KhRMrPjDAoA0ScmyoxzbrFzrk+1atV8RwGObsCAopWZe+8t\n0uYOHTqk7t27a8qUKRo2bJgee+wxigyAuBUTZQaIeHXqSHPnSlWq/L7UVKwYXD53bnBcIXJyctSl\nSxelpaXpkUce0UMPPUSRARDXKDNAuDRrJm3cKPXp89tPAO7TJ7i8WbNCN5Gdna0OHTpo9uzZGjFi\nhP73f/83DMEBILKZc853hlJTv359t379et8xgDJx4MABtW/fXunp6Xrqqad0zz33+I4EAGXKzN5x\nztUvbByfqAVEgf3796tNmzZasWKFxo8fr759+/qOBAARgzIDRLi9e/cqNTVVGRkZeu6559SzZ8/C\nbwQAcYQyA0Sw3bt3q0WLFnr99df1/PPPq0uXLr4jAUDEocwAEWrXrl1q1qyZ1q1bpxkzZqhDhw6+\nIwFARKLMABHop59+UnJysjZs2KA5c+aobdu2viMBQMSizAARZufOnWrSpIk++ugjzZ8/Xy1btvQd\nCQAiGmUGiCA//PCDGjdurC1btmjRokVKTk72HQkAIh5lBogQ3377rZKSkvT111/rlVdeUSAQ8B0J\nAKICZQaIAN98840CgYC+//57LV26VA0bNvQdCQCiBmUG8OzLL79UIBDQjz/+qBUrVuj666/3HQkA\nogplBvBoy5YtCgQC2r17t1atWqVrrrnGdyQAiDqUGcCTzZs3KxAI6ODBg1q7dq2uuuoq35EAICpR\nZgAPNm3apKSkJDnnlJGRocsuu8x3JACIWuV8BwDizYYNG5SQkKBy5cpRZACgFMREmTGzVDObtGvX\nLt9RgAK98847SkxMVOXKlZWZmak//elPviMBQNSLiTLjnFvsnOtTrVo131GAY/rPf/6jpKQknXLK\nKcrKytJFF13kOxIAxISYKDNApHvttdfUpEkTVa9eXVlZWapdu7bvSAAQMygzQBnLyMhQSkqKzjrr\nLGVlZalWrVq+IwFATKHMAGVo5cqVat68uc4//3xlZmbqnHPO8R0JAGIOZQYoI0uWLFFqaqouuugi\nZWRk6Mwzz/QdCQBiEmUGKAMLFy5UmzZtdOmll2rNmjWqUaOG70gAELMoM0Ape+mll3TjjTeqXr16\nWr16tapXr+47EgDENMoMUIpmzJihjh076rrrrtOKFSt06qmn+o4EADGPMgOUkmnTpqlLly5q2LCh\nli1bplNOOcV3JACIC5QZoBRMmjRJPXr0UOPGjfXKK6/opJNO8h0JAOIGZQY4TmPHjtVtt92m5s2b\na9GiRapSpYrvSAAQVygzwHEYNWqU7rrrLrVu3Vrz589X5cqVfUcCgLhDmQFK6LHHHtOAAQN04403\n6qWXXtIJJ5zgOxIAxCXKDFBMzjk99NBDGjJkiG6++WbNnDlTFStW9B0LAOJWBd8BgGjinNPQoUP1\nz3/+U927d9fkyZNVvnx537EAIK5RZoAics5p4MCBevLJJ9W7d29NmDBB5cqxcxMAfOM3MVAEzjnd\nc889evLJJ3XHHXdQZAAggvDbGChEbm6u+vbtqzFjxui+++7TmDFjKDIAEEH4jQwU4PDhw+rVq5cm\nTZqkv//97xo5cqTMzHcsAEAevGcGOIZDhw7pllt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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(9, 9))\n", "\n", From 0cfbb40858c5679bb030144b1f6cda430175bb90 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Mon, 17 Jun 2019 17:13:52 -0400 Subject: [PATCH 22/26] Moved the skycalc querying to its own script, atmospheric_transmittance.py --- atmospheric_transmittance.py | 194 ++++++++++++++++++++++ observing_examples.ipynb | 309 +++++++++++------------------------ 2 files changed, 290 insertions(+), 213 deletions(-) create mode 100644 atmospheric_transmittance.py diff --git a/atmospheric_transmittance.py b/atmospheric_transmittance.py new file mode 100644 index 0000000..39716d3 --- /dev/null +++ b/atmospheric_transmittance.py @@ -0,0 +1,194 @@ +import os +import json +import requests + +import astropy.units as u +from astropy.io import fits + + +def get(airmass=1.0, pwv_mode='pwv', season=0, time=0, pwv=3.5, msolflux=130.0, + incl_moon='Y', moon_sun_sep=90.0, moon_target_sep=45.0, moon_alt=45.0, + moon_earth_dist=1.0, incl_starlight='Y', incl_zodiacal='Y', + ecl_lon=135.0, ecl_lat=90.0, incl_loweratm='Y', incl_upperatm='Y', + incl_airglow='Y', incl_therm='N', therm_t1=0.0, therm_e1=0.0, + therm_t2=0.0, therm_e2=0.0, therm_t3=0.0, therm_e3=0.0, vacair='vac', + wmin=300.0, wmax=2000.0, wgrid_mode='fixed_wavelength_step', + wdelta=0.1, wres=20000, lsf_type='none', lsf_gauss_fwhm=5.0, + lsf_boxcar_fwhm=5.0, observatory='paranal'): + """ + Returns the model atmospheric transmittance curve queried from the SkyCalc + Sky Model Calculator. The default parameters used here are the default + parameters provided by SkyCalc: + http://www.eso.org/observing/etc/doc/skycalc/skycalc_defaults.txt + + Parameters + ---------- + airmass: float (range [1.0,3.0]) + Airmass. Alt and airmass are coupled through the plane parallel + approximation airmass=sec(z), z being the zenith distance + z=90°−Alt + pwv_mode: str + options: ['pwv','season'] (default is 'pwv') + season: int + Time of year if not in pwv mode. + options: [0,1,2,3,4,5,6] (default is 0) + (0 = all year, 1 = dec/jan, 2 = feb/mar...) + time: int + Period of night. options: [0,1,2,3] (default is 0) + (0 = all year, 1, 2, 3 = third of night) + pwv: float + Precipitable Water Vapor (default is 3.5). + options: [-1.0,0.5,1.0,1.5,2.5,3.5,5.0,7.5,10.0,20.0] + msolflux: float + Monthly Averaged Solar Flux, s.f.u float > 0 (default is 130.0) + incl_moon: str + Flag for inclusion of scattered moonlight. options = ['Y', 'N'] + (default is 'Y') + Moon coordinate constraints: |z – zmoon| ≤ ρ ≤ |z + zmoon| where + ρ=moon/target separation, z=90°−target altitude and + zmoon=90°−moon altitude. + moon_sun_sep: float + Degrees of separation between Sun and Moon as seen from Earth + (i.e. the "moon phase"). + options: [0.0,360.0] (default is 90.0) + moon_target_sep: float + Moon-Target Separation ( ρ ) + Degrees in range [0.0,180.0] (defualt is 45.0) + # degrees float range [-90.0,90.0] Moon Altitude over Horizon + moon_alt: float + Moon Altitude over Horizon. Degrees in range [-90.0,90.0] + (default is 45.0) + moon_earth_dist: float + Moon-Earth Distance (mean=1) in range [0.91,1.08] + (default is 1.0) + incl_starlight: str + Flag for inclusion of scattered starlight. + options: ['Y', 'N'] (default is 'Y') + incl_zodiacal: str + Flag for inclusion of zodiacal light. + options: ['Y', 'N'] (default is 'Y') + ecl_lon: float + Heliocentric ecliptic in degree range [-180.0,180.0]. + (default is 135.0) + ecl_lat: float + Ecliptic latitude in degree range [-90.0,90.0]. + (default is 90.0) + + incl_loweratm: str + Flag for inclusion of molecular emission of lower atmosphere. + options: ['Y', 'N'] (default is 'Y') + incl_upperatm: str + Flag for inclusion of molecular emission of upper atmosphere. + options: ['Y', 'N'] (default is 'Y') + incl_airglow: str + Flag for inclusion of airglow continuum (residual continuum) + options: ['Y', 'N'] (default is 'Y') + + incl_therm: str + Flag for inclusion of instrumental thermal radiation. + options: ['Y', 'N'] (default is 'N') + Note: + Instrumental Thermal Emission. This radiance component represents + an instrumental effect. The emission is provided relative to the + other model components. To obtain the correct absolute flux, an + instrumental response curve must be applied to the resulting + model spectrum. See section 6.2.4 in the SkyCalc documentation at + http://localhost/observing/etc/doc/skycalc/ + The_Cerro_Paranal_Advanced_Sky_Model.pdf + therm_t1, therm_t2, therm_t3 : float + Temperature in K (default is 0.0) + therm_e1, therm_e2, therm_e3: float + In range [0,1] (default is 0.0) + + vacair: str + In regards to the wavelength grid. + options: ['vac', 'air] (default is 'vac') + wmin: float + Minimum wavelength (nm) in the wavelength grid. + Must be in range [300.0,30000.0] and < wmax + (default is 300.0) + wmax: float + Maximum wavelength (nm) in the wavelength grid. + Must be in range [300.0,30000.0] and > wmin + (default is 2000.0) + wgrid_mode: str + Mode of the wavelength grid. + options: ['fixed_spectral_resolution','fixed_wavelength_step', 'user'] + (default is 'fixed_wavelength_step') + wdelta: float + Wavelength sampling step dlam in range [0,30000.0] (nm/step) + (default is 0.1) + wres: int + lam/dlam where dlam is wavelength step. + In range [0,1.0e6] (default is 20000) + wgrid_user: list of floats + default is [500.0, 510.0, 520.0, 530.0, 540.0, 550.0] + + lsf_type: str + Line spread function type for convolution. + options: ['none','Gaussian','Boxcar'] (default is 'none') + lsf_gauss_fwhm: float + Gaussian full-width half-max for line spread function wavelength bins. + Range > 0.0 (default is 5.0) + lsf_boxcar_fwhm: float + Boxcar full-width half-max for line spread function wavelength bins. + Range > 0.0 (default is 5.0) + + observatory: str + Observatory where observation takes place. + Options are 'paranal', 'lasilla', 'armazones' (default is 'paranal') + + Returns + ------- + trans_waves, transmission: tuple of arrays of floats + 'trans_waves' is an array of wavelengths in angstroms (float), + 'transmission' is an array of fractional atmospheric + transmittance (float). + + """ + + params = locals() + + if params['observatory'] == 'lasilla': + params['observatory'] = '2400' + elif params['observatory'] == 'paranal': + params['observatory'] = '2640' + elif (params['observatory'] == '3060m' or + params['observatory'] == 'armazones'): + params['observatory'] = '3060' + else: + raise ValueError('Wrong Observatory name, please refer to the ' + 'skycalc_cli documentation.') + + # Use the bit from skycalc_cli which queries from the SkyCalc Sky Model + server = 'http://etimecalret-001.eso.org' + url = server + '/observing/etc/api/skycalc' + response = requests.post(url, data=json.dumps(params)) + results = json.loads(response.text) + + status = results['status'] + tmpdir = results['tmpdir'] + tmpurl = server + '/observing/etc/tmp/' + tmpdir + '/skytable.fits' + + if status == 'success': + try: + response = requests.get(tmpurl, stream=True) + data = response.content + except requests.exceptions.RequestException as e: + print(e, 'could not retrieve FITS data from server') + else: + print('HTML request failed', results) + + tmp_data_file = './tmp_skycalc_data.fits' + + with open(tmp_data_file, 'wb') as f: + f.write(data) + + hdu = fits.open(tmp_data_file) + trans_waves = hdu[1].data["LAM"] * u.um # wavelengths + transmission = hdu[1].data["TRANS"] + + # Delete the file after reading from it + os.remove(tmp_data_file) + + return trans_waves.to(u.angstrom), transmission diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 2a73521..347b506 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -61,10 +61,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -103,10 +101,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -114,38 +110,6 @@ "text": [ "Query finished.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", - "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", - "WARNING: W35: None:7:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", - "WARNING: W35: None:8:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", - "WARNING: W35: None:10:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", - "WARNING: W27: None:11:0: W27: COOSYS deprecated in VOTable 1.2 [astropy.io.votable.tree]\n", - "WARNING: W06: None:48:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", - "WARNING: W50: None:51:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", - "WARNING: W50: None:54:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", - "WARNING: W50: None:57:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", - "WARNING: W50: None:60:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", - "WARNING: W50: None:123:0: W50: Invalid unit string 'mas**-2' [astropy.io.votable.tree]\n", - "WARNING: W50: None:126:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", - "WARNING: W06: None:129:0: W06: Invalid UCD 'em.wavenumber;stat.error': Primary word 'stat.error' is not valid as a secondary word [astropy.io.votable.tree]\n", - "WARNING: W50: None:129:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", - "WARNING: W06: None:141:0: W06: Invalid UCD 'pos.errorEllipse;stat.max': Secondary word 'pos.errorEllipse' is not valid as a primary word [astropy.io.votable.tree]\n", - "WARNING: W50: None:156:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", - "WARNING: W50: None:159:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", - "WARNING: W06: None:162:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", - "WARNING: W50: None:171:0: W50: Invalid unit string ''electron'.s**-1' (suppressing further warnings of this type...) [astropy.io.votable.tree]\n", - "WARNING: W06: None:177:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", - "WARNING: W06: None:192:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", - "WARNING: W06: None:204:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", - "WARNING: W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", - "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", - "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" - ] } ], "source": [ @@ -178,10 +142,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "T_eff = round(T_eff, -2) # round to nearest 100 K\n", @@ -207,19 +169,19 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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J0yFQjPR5YjHenTgQXWOUkIniQSh3IjV8fqrCqhsZ42RQbpK74RvTISSUWLXOKs/lf/4E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\nTkSuQmCC\n", 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -257,10 +219,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", @@ -285,20 +245,21 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -331,69 +292,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Model the sky by querying results from the SkyCalc Sky Model Calculator:" + "Model the atmospheric transmittance using the local script `atmospheric_transmittance.py`, which queries results from the SkyCalc Sky Model Calculator. " ] }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://etimecalret-001.eso.org/observing/etc/tmp/0bbc552a-7c09-403f-ac88-b620b6a61bc9/skytable.fits\n" + ] + } + ], "source": [ - "# SkyCalc recognizes Cerro Paranal by its id number 2460, which we \n", - "# got from the skycalc_cli source code\n", - "paranal_id = '2640'\n", + "import atmospheric_transmittance as at\n", "\n", - "# Use the default parameters from skycalc to set up the parameter dict\n", - "params_path = download_file('http://www.eso.org/observing/etc/' + \\\n", - " 'doc/skycalc/skycalc_defaults.txt') \n", - "\n", - "params = {}\n", - "with open(params_path) as o:\n", - " for row in o.readlines():\n", - " columns = row.split(':')\n", - " columns = [column.strip() for column in columns]\n", - " key, value = columns\n", - " \n", - " # convert numbered values to floats/ints\n", - " try:\n", - " value = int(value)\n", - " except:\n", - " try:\n", - " value = float(value)\n", - " except:\n", - " pass\n", - " \n", - " params[key] = value\n", - " \n", - " if key == \"observatory\":\n", - " params['observatory'] = paranal_id\n", - " \n", - "# edit the parameters to match your observation\n", - "params['airmass'] = 1.5\n", - "\n", - "# Use the bit from skycalc_cli which queries from the SkyCalc Sky Model\n", - "server = 'http://etimecalret-001.eso.org'\n", - "url = server + '/observing/etc/api/skycalc'\n", - "\n", - "response = requests.post(url, data=json.dumps(params))\n", - "results = json.loads(response.text)\n", - "\n", - "status = results['status']\n", - "tmpdir = results['tmpdir']\n", - "tmpurl = server + '/observing/etc/tmp/' + \\\n", - " tmpdir + '/skytable.fits'\n", - "\n", - "if status == 'success':\n", - " try: \n", - " response = requests.get(tmpurl, stream=True)\n", - " data = response.content\n", - " except requests.exceptions.RequestException as e:\n", - " print(e, 'could not retrieve FITS data from server')\n", - "else:\n", - " print('HTML request failed', results)" + "trans_waves, transmission = at.get(airmass=1.5)" ] }, { @@ -403,22 +321,6 @@ "Write the data requested from the SkyCalc site to a fits file so it can be read in a human readable format:" ] }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "with open('data.fits', 'wb') as f:\n", - " f.write(data)\n", - " \n", - "hdu = fits.open('data.fits')\n", - "trans_waves = hdu[1].data[\"LAM\"] * u.um # wavelengths\n", - "transmission = hdu[1].data[\"TRANS\"]" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -428,19 +330,19 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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FEZkM/B7IA/5qjJke8XwF8AjQwd5nmjFmZrMDZbGGBsMvX1namAjKSgq4dmJ/bjplcJMp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\n", 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -468,19 +370,19 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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UQoJxy3m9WF1eoblGJGzCmUQ2AAUh6538skOY2bnAL4BLnHMHDrcj59wk51yx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\nf8MbCC8XeMEvM+A3zrmHjvK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+ "image/png": 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -508,19 +410,19 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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Y/nzZ/o56/MLP08DS8G/hEWBZ2d/KQX8j9bbPoXwRyWM3fgnMPZLHOPM//IwP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\n", 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -541,10 +443,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "band = 'rprime' # only want to know about the r' band\n", @@ -569,10 +469,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 36, + "metadata": {}, "outputs": [], "source": [ "countrate = observation.countrate(area=aperture_area) / gain" @@ -587,10 +485,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -627,10 +523,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", @@ -655,20 +549,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 39, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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YsGSL7+esbLILenLO6wYcG+j5TcWS5VkT4x3PxsSKSBowEjgfyAFmi8hYVV0S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\nAABJRU5ErkJggg==\n", 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\n", 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    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -693,10 +588,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -737,19 +630,19 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 41, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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tPxGn32X8MZ9FGKkf8sRu8TXm2IkID1w4iKWFe/nJK86AjdnpSV6HFdaaOxR8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\nRCTX3ZYmIv8WkXXuz1R3u4j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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -768,17 +661,15 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": 42, + "metadata": {}, "outputs": [], "source": [ "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", " 'trappist1': {'spectrum': photlam_trappist1}\n", " }\n", "\n", - "for star in stars.keys():\n", + "for star in stars:\n", " stars[star]['observation'] = Observation(stars[star]['spectrum'], kepler_response)" ] }, @@ -791,16 +682,14 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 43, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "synphot counts in 10s exposure:\n", + "counts predicted by synphot in 10s exposure:\n", "\t HAT-P-11: 24.7 million\n", "\t TRAPPIST-1 65.0 thousand\n" ] @@ -809,11 +698,11 @@ "source": [ "kepler_area = np.pi * (1.4 * u.m / 2) ** 2 # area of Kepler's primary mirror\n", "\n", - "for star in stars.keys():\n", + "for star in stars:\n", " countrate = stars[star]['observation'].countrate(area=kepler_area)\n", " stars[star]['counts'] = countrate * 10 * u.s\n", " \n", - "print('synphot counts in 10s exposure:')\n", + "print('counts predicted by synphot in 10s exposure:')\n", "print('\\t HAT-P-11:', round(stars['hatp11']['counts'].value / 1e6, 1), 'million')\n", "print('\\t TRAPPIST-1', round(stars['trappist1']['counts'].value / 1e3, 1), 'thousand')" ] @@ -830,15 +719,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We know from real observations (with AGILE on APO's 3.5m telescope and with Kepler) that the actual count values for these stars taken on certain dates are:" + "We know from real observations (with ARCTIC on APO's 3.5m telescope and with Kepler) that the actual count values for these stars taken on certain dates are:" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 44, + "metadata": {}, "outputs": [], "source": [ "hatp11_r_apo = 34000000\n", @@ -849,10 +736,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 45, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -885,29 +770,29 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 46, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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KgFdeeYXXXnuNSpUqJRcXgE6dOlGiRAl8fHyoWLEiEyZMwMvLi6lTp/Lqq69S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\nQBnjPTOlJCcnR127dtXs2bM1fPhwDR061HckAADiAmWmFGR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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -939,9 +824,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -949,7 +832,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -963,7 +846,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.1" } }, "nbformat": 4, From 805c5e96a8c3b4cdfc704319fb0a169f46c3a0a2 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Mon, 17 Jun 2019 18:27:27 -0400 Subject: [PATCH 23/26] Implemented tynt for querying filter transmittance --- atmospheric_transmittance.py | 12 +-- observing_examples.ipynb | 161 +++++++++++++++++++---------------- 2 files changed, 94 insertions(+), 79 deletions(-) diff --git a/atmospheric_transmittance.py b/atmospheric_transmittance.py index 39716d3..c8fe76a 100644 --- a/atmospheric_transmittance.py +++ b/atmospheric_transmittance.py @@ -87,12 +87,11 @@ def get(airmass=1.0, pwv_mode='pwv', season=0, time=0, pwv=3.5, msolflux=130.0, incl_therm: str Flag for inclusion of instrumental thermal radiation. options: ['Y', 'N'] (default is 'N') - Note: - Instrumental Thermal Emission. This radiance component represents - an instrumental effect. The emission is provided relative to the - other model components. To obtain the correct absolute flux, an - instrumental response curve must be applied to the resulting - model spectrum. See section 6.2.4 in the SkyCalc documentation at + Note: This radiance component represents an instrumental effect. + The emission is provided relative to the other model components. + To obtain the correct absolute flux, an instrumental response curve + must be applied to the resulting model spectrum. + See section 6.2.4 in the SkyCalc documentation at http://localhost/observing/etc/doc/skycalc/ The_Cerro_Paranal_Advanced_Sky_Model.pdf therm_t1, therm_t2, therm_t3 : float @@ -179,6 +178,7 @@ def get(airmass=1.0, pwv_mode='pwv', season=0, time=0, pwv=3.5, msolflux=130.0, else: print('HTML request failed', results) + # Create a temporary file to write the binary results to tmp_data_file = './tmp_skycalc_data.fits' with open(tmp_data_file, 'wb') as f: diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 347b506..56341bd 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -61,27 +61,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import requests\n", - "import json\n", "\n", "import astropy.units as u\n", - "from astropy.io import fits\n", - "from astropy.utils.data import download_file\n", - "from astropy.coordinates import SkyCoord\n", - "\n", - "from astroquery.gaia import Gaia\n", - "\n", - "from synphot import units\n", - "from synphot.models import Empirical1D\n", - "from synphot.spectrum import SourceSpectrum, SpectralElement\n", - "from synphot.observation import Observation" + "from synphot import units" ] }, { @@ -96,12 +85,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we get the star's parameters using astroquery's Gaia query: " + "First we get the star's parameters using astroquery's Gaia query and astropy's SkyCoord: " ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -113,6 +102,9 @@ } ], "source": [ + "from astropy.coordinates import SkyCoord\n", + "from astroquery.gaia import Gaia\n", + "\n", "ID = 'HAT-P-11'\n", "\n", "coord = SkyCoord.from_name(ID)\n", @@ -142,10 +134,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ + "from astropy.io import fits\n", + "\n", "T_eff = round(T_eff, -2) # round to nearest 100 K\n", "\n", "url = ('ftp://phoenix.astro.physik.uni-goettingen.de/v2.0/HiResFITS/'\n", @@ -169,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -186,6 +180,9 @@ } ], "source": [ + "from synphot.spectrum import SourceSpectrum\n", + "from synphot.models import Empirical1D\n", + "\n", "photlam_hatp11 = SourceSpectrum(Empirical1D,\n", " points=wavelengths, lookup_table=flux)\n", "\n", @@ -219,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -239,20 +236,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For the bandpass we will use the SDSS r- and z- filters whose transmission functions we query from SVO. \n", - "

    To construct a bandpass from a file with synphot, use SpectralElement:

    " + "The bandpasses we will use are the SDSS r- and z- filters, whose transmission functions we query from SVO using the tynt package. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To construct a bandpass from an array with `synphot`, use SpectralElement with the Empirical1D model:" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 64, "metadata": { "scrolled": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: W42: /var/folders/jg/nxrb0cds7_d1w0z9ldk5b7f40000gn/T/tmpvk8kpegy:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n", + "WARNING: W42: /var/folders/jg/nxrb0cds7_d1w0z9ldk5b7f40000gn/T/tmpjtdgwjz5:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -264,14 +275,19 @@ } ], "source": [ + "import tynt\n", + "from synphot.spectrum import SpectralElement\n", + "\n", "sdss = ['rprime', 'zprime'] # only want r' and z' bands for now\n", - "svo_sdss_link = 'http://svo2.cab.inta-csic.es/theory/fps' + \\\n", - " '/getdata.php?format=ascii&id=SLOAN/SDSS.'\n", + "\n", + "f = tynt.FilterGenerator()\n", "\n", "bandpasses = {}\n", "for band in sdss:\n", - " local_path = download_file(svo_sdss_link + band + '_filter')\n", - " bp = SpectralElement.from_file(local_path)\n", + " filt = f.download_true_transmittance('SLOAN/SDSS.' + band + '_filter')\n", + " bp = SpectralElement(Empirical1D,\n", + " points=filt.wavelength,\n", + " lookup_table=filt.transmittance)\n", " bandpasses[band] = bp\n", " \n", " plt.plot(bp.waveset, bp(bp.waveset), label=band)\n", @@ -280,6 +296,13 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also construct a bandpass from a file using synphot's SpectralElement.from_file()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -292,22 +315,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Model the atmospheric transmittance using the local script `atmospheric_transmittance.py`, which queries results from the SkyCalc Sky Model Calculator. " + "Model the atmospheric transmittance using the local script `atmospheric_transmittance.py`, which queries results from the SkyCalc Sky Model Calculator:" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 65, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "http://etimecalret-001.eso.org/observing/etc/tmp/0bbc552a-7c09-403f-ac88-b620b6a61bc9/skytable.fits\n" - ] - } - ], + "outputs": [], "source": [ "import atmospheric_transmittance as at\n", "\n", @@ -318,19 +333,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Write the data requested from the SkyCalc site to a fits file so it can be read in a human readable format:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Then we combine with `synphot` so we can convolve the atmospheric effects with other parts of the observation:" + "Use `synphot` to convolve the atmospheric effects with other parts of the observation:" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -370,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -410,12 +418,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -443,10 +451,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ + "from synphot.observation import Observation\n", + "\n", "band = 'rprime' # only want to know about the r' band\n", "\n", "observation = Observation(photlam_hatp11, bp_atmos_qe[band])" @@ -469,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -485,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -523,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -549,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 73, "metadata": { "scrolled": true }, @@ -588,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -630,12 +640,19 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 81, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: W42: /var/folders/jg/nxrb0cds7_d1w0z9ldk5b7f40000gn/T/tmprbak7z13:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
    " ] @@ -647,8 +664,13 @@ } ], "source": [ - "kepler_response_path = download_file('https://keplergo.arc.nasa.gov/kepler_response_hires1.txt')\n", - "kepler_response = SpectralElement.from_file(kepler_response_path, wave_unit=u.nm)\n", + "f = tynt.FilterGenerator()\n", + "filt = f.download_true_transmittance('Kepler/Kepler.K')\n", + "\n", + "kepler_response = SpectralElement(Empirical1D,\n", + " points=filt.wavelength,\n", + " lookup_table=filt.transmittance)\n", + "\n", "kepler_response.plot()" ] }, @@ -661,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -682,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -724,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -736,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -770,16 +792,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 46, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, @@ -820,13 +842,6 @@ "\n", "plt.legend()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 6cd0d13f3136980d7684093698c438fad9351e51 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Tue, 18 Jun 2019 10:56:02 -0400 Subject: [PATCH 24/26] Renamed atmospheric_transmittance.py to skymodel.py and changed the function name --- observing_examples.ipynb | 96 +++++++++++++++------ atmospheric_transmittance.py => skymodel.py | 26 ++++-- 2 files changed, 86 insertions(+), 36 deletions(-) rename atmospheric_transmittance.py => skymodel.py (85%) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 56341bd..743dfdf 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -62,7 +62,9 @@ { "cell_type": "code", "execution_count": 23, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline\n", @@ -91,7 +93,9 @@ { "cell_type": "code", "execution_count": 24, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -135,7 +139,9 @@ { "cell_type": "code", "execution_count": 25, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from astropy.io import fits\n", @@ -164,7 +170,9 @@ { "cell_type": "code", "execution_count": 26, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -217,7 +225,9 @@ { "cell_type": "code", "execution_count": 27, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "aperture_radius = 3.5 / 2 * u.m # radius of 3.5m ARC telescope at APO\n", @@ -250,6 +260,7 @@ "cell_type": "code", "execution_count": 64, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ @@ -315,18 +326,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Model the atmospheric transmittance using the local script `atmospheric_transmittance.py`, which queries results from the SkyCalc Sky Model Calculator:" + "Model the atmospheric transmittance using the local script `skymodel.py`, which queries results from the SkyCalc Sky Model Calculator:" ] }, { "cell_type": "code", - "execution_count": 65, - "metadata": {}, + "execution_count": 1, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "import atmospheric_transmittance as at\n", + "import skymodel\n", "\n", - "trans_waves, transmission = at.get(airmass=1.5)" + "trans_waves, transmission = skymodel.get_atmospheric_transmittance(airmass=1.5)" ] }, { @@ -339,7 +352,9 @@ { "cell_type": "code", "execution_count": 66, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -366,7 +381,7 @@ "metadata": {}, "source": [ "\n", - "## 6. Model the affect of the CCD's quantum efficiency on the spectrum" + "## 6. Model the effect of the CCD's quantum efficiency on the spectrum" ] }, { @@ -379,7 +394,9 @@ { "cell_type": "code", "execution_count": 67, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -419,7 +436,9 @@ { "cell_type": "code", "execution_count": 68, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -452,7 +471,9 @@ { "cell_type": "code", "execution_count": 69, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from synphot.observation import Observation\n", @@ -480,7 +501,9 @@ { "cell_type": "code", "execution_count": 70, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "countrate = observation.countrate(area=aperture_area) / gain" @@ -496,7 +519,9 @@ { "cell_type": "code", "execution_count": 71, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -534,7 +559,9 @@ { "cell_type": "code", "execution_count": 72, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "T_eff = 2600 # approx temperature of trappist-1 [K], Gillon 2017\n", @@ -561,6 +588,7 @@ "cell_type": "code", "execution_count": 73, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ @@ -599,7 +627,9 @@ { "cell_type": "code", "execution_count": 74, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -641,7 +671,9 @@ { "cell_type": "code", "execution_count": 81, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stderr", @@ -684,7 +716,9 @@ { "cell_type": "code", "execution_count": 82, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "stars = {'hatp11': {'spectrum': photlam_hatp11},\n", @@ -705,7 +739,9 @@ { "cell_type": "code", "execution_count": 83, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -747,7 +783,9 @@ { "cell_type": "code", "execution_count": 84, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "hatp11_r_apo = 34000000\n", @@ -759,7 +797,9 @@ { "cell_type": "code", "execution_count": 85, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -793,7 +833,9 @@ { "cell_type": "code", "execution_count": 86, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -847,7 +889,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -861,7 +903,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/atmospheric_transmittance.py b/skymodel.py similarity index 85% rename from atmospheric_transmittance.py rename to skymodel.py index c8fe76a..b2bd572 100644 --- a/atmospheric_transmittance.py +++ b/skymodel.py @@ -6,15 +6,23 @@ from astropy.io import fits -def get(airmass=1.0, pwv_mode='pwv', season=0, time=0, pwv=3.5, msolflux=130.0, - incl_moon='Y', moon_sun_sep=90.0, moon_target_sep=45.0, moon_alt=45.0, - moon_earth_dist=1.0, incl_starlight='Y', incl_zodiacal='Y', - ecl_lon=135.0, ecl_lat=90.0, incl_loweratm='Y', incl_upperatm='Y', - incl_airglow='Y', incl_therm='N', therm_t1=0.0, therm_e1=0.0, - therm_t2=0.0, therm_e2=0.0, therm_t3=0.0, therm_e3=0.0, vacair='vac', - wmin=300.0, wmax=2000.0, wgrid_mode='fixed_wavelength_step', - wdelta=0.1, wres=20000, lsf_type='none', lsf_gauss_fwhm=5.0, - lsf_boxcar_fwhm=5.0, observatory='paranal'): +def get_atmospheric_transmittance(airmass=1.0, pwv_mode='pwv', season=0, + time=0, pwv=3.5, msolflux=130.0, + incl_moon='Y', moon_sun_sep=90.0, + moon_target_sep=45.0, moon_alt=45.0, + moon_earth_dist=1.0, incl_starlight='Y', + incl_zodiacal='Y', + ecl_lon=135.0, ecl_lat=90.0, + incl_loweratm='Y', incl_upperatm='Y', + incl_airglow='Y', incl_therm='N', + therm_t1=0.0, therm_e1=0.0, + therm_t2=0.0, therm_e2=0.0, therm_t3=0.0, + therm_e3=0.0, vacair='vac', wmin=300.0, + wmax=2000.0, + wgrid_mode='fixed_wavelength_step', + wdelta=0.1, wres=20000, lsf_type='none', + lsf_gauss_fwhm=5.0, lsf_boxcar_fwhm=5.0, + observatory='paranal'): """ Returns the model atmospheric transmittance curve queried from the SkyCalc Sky Model Calculator. The default parameters used here are the default From e56e7ac76ff971b3a9b5aab0b30df60ef3d1a3bf Mon Sep 17 00:00:00 2001 From: tcjansen Date: Wed, 17 Jul 2019 12:48:36 -0400 Subject: [PATCH 25/26] Added snr calculation examples --- observing_examples.ipynb | 385 ++++++++++++++++++++++++++++++++------- 1 file changed, 322 insertions(+), 63 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index 743dfdf..b3c216f 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -53,6 +53,7 @@ "
  • Model the effect of the quantum efficiency
    • \n", "
  • Combine all of the effects and \"observe\"
    • \n", "
  • Compute the count rate and total counts
    • \n", + "
  • Estimate the expected signal to noise ratio
    • \n", "
  • Look at another example, this time with TRAPPIST-1
    • \n", "
  • Simulate observations with a space-based telescope
    • \n", "
  • Compare simulated counts to observed counts
    • \n", @@ -61,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 44, "metadata": { "collapsed": true }, @@ -92,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 45, "metadata": { "collapsed": false }, @@ -103,6 +104,38 @@ "text": [ "Query finished.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: W35: None:5:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:6:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:7:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:8:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W35: None:10:0: W35: 'value' attribute required for INFO elements [astropy.io.votable.tree]\n", + "WARNING: W27: None:11:0: W27: COOSYS deprecated in VOTable 1.2 [astropy.io.votable.tree]\n", + "WARNING: W06: None:48:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:51:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:54:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:57:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:60:0: W50: Invalid unit string 'mas.yr**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:123:0: W50: Invalid unit string 'mas**-2' [astropy.io.votable.tree]\n", + "WARNING: W50: None:126:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:129:0: W06: Invalid UCD 'em.wavenumber;stat.error': Primary word 'stat.error' is not valid as a secondary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:129:0: W50: Invalid unit string 'um**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:141:0: W06: Invalid UCD 'pos.errorEllipse;stat.max': Secondary word 'pos.errorEllipse' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:156:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", + "WARNING: W50: None:159:0: W50: Invalid unit string ''electron'.s**-1' [astropy.io.votable.tree]\n", + "WARNING: W06: None:162:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W50: None:171:0: W50: Invalid unit string ''electron'.s**-1' (suppressing further warnings of this type...) [astropy.io.votable.tree]\n", + "WARNING: W06: None:177:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W06: None:192:0: W06: Invalid UCD 'arith.ratio': Secondary word 'arith.ratio' is not valid as a primary word [astropy.io.votable.tree]\n", + "WARNING: W06: None:204:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:207:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:210:0: W06: Invalid UCD 'phot.color': Unknown word 'phot.color' [astropy.io.votable.tree]\n", + "WARNING: W06: None:282:0: W06: Invalid UCD 'phys.size.radius;stat.error': Primary word 'stat.error' is not valid as a secondary word (suppressing further warnings of this type...) [astropy.io.votable.tree]\n" + ] } ], "source": [ @@ -138,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -169,21 +202,19 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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J0yFQjPR5YjHenTgQXWOUkIniQSh3IjV8fqrCqhsZ42RQbpK74RvTISSUWLXOKs/lf/4E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\netv7VqnqN6p6EtaQFx/Y2zf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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -224,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 48, "metadata": { "collapsed": true }, @@ -258,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 49, "metadata": { "collapsed": false, "scrolled": true @@ -268,20 +299,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING: W42: /var/folders/jg/nxrb0cds7_d1w0z9ldk5b7f40000gn/T/tmpvk8kpegy:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n", - "WARNING: W42: /var/folders/jg/nxrb0cds7_d1w0z9ldk5b7f40000gn/T/tmpjtdgwjz5:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" + "WARNING: W42: /var/folders/bn/d7v8bk_16cjg8qcm1gj03m6h0000gp/T/tmp46vg3hd6:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n", + "WARNING: W42: /var/folders/bn/d7v8bk_16cjg8qcm1gj03m6h0000gp/T/tmphlz0oi7e:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" ] }, { "data": { - "image/png": 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\n", 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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -331,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 50, "metadata": { "collapsed": true }, @@ -351,21 +380,19 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", 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F5t1w6bg+XofgGS2VKS8kWn30ANYKabUAxpj5WBPcZa33V25lcNdSykqSq/8N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\nvtbKYTn16dm2z0oKnTQpqCx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\n", + "image/png": 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\n", 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"text/plain": [ "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -470,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 54, "metadata": { "collapsed": true }, @@ -500,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 55, "metadata": { "collapsed": true }, @@ -518,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 56, "metadata": { "collapsed": false }, @@ -533,18 +556,113 @@ } ], "source": [ - "counts_hatp11 = countrate * 10 * u.s\n", + "exptime = 10 * u.s\n", + "counts_hatp11 = countrate * exptime\n", "\n", "print(\"HAT-P-11 counts in the r-band, 10 s exposure:\")\n", "print(int(counts_hatp11.value), \"cts\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 9. Signal to noise ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To estimate the signal to noise ratio of this observation, input the total counts into `synphot`'s `howell_snr` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR of HAT-P-11 in 10 s exposure:\n" + ] + }, + { + "data": { + "text/latex": [ + "$6193.0315 \\; \\mathrm{ct^{1/2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from synphot.observation import howell_snr\n", + "\n", + "print(\"SNR of HAT-P-11 in 10 s exposure:\")\n", + "howell_snr(counts_hatp11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also use `synphot` to calculate the exposure time needed to obtain a certain SNR:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure time needed to obtain SNR > 100:\n" + ] + }, + { + "data": { + "text/latex": [ + "$0.0026073145 \\; \\mathrm{s}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from synphot.observation import exptime_from_howell_snr\n", + "\n", + "desired_snr = 100 * np.sqrt(1 * u.ct)\n", + "\n", + "print(\"Exposure time needed to obtain SNR > 100:\")\n", + "exptime_from_howell_snr(desired_snr, countrate)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", - "## 9. Another example, this time with TRAPPIST-1" + "## 10. Another example, this time with TRAPPIST-1" ] }, { @@ -558,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 59, "metadata": { "collapsed": true }, @@ -586,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 60, "metadata": { "collapsed": false, "scrolled": true @@ -594,14 +712,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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YsGSL7+esbLILenLO6wYcG+j5TcWS5VkT4x3PxsSKSBowEjgfyAFmi8hYVV0S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\nqo4F3gDecQcP7MRJJLj1xuA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"text/plain": [ "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -626,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 61, "metadata": { "collapsed": false }, @@ -656,7 +772,154 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 10. Predicting photon counts of the same stars, this time observed by a space-based telescope" + "Compute the signal to noise ratio:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR of TRAPPIST-1 in 10 s exposure:\n" + ] + }, + { + "data": { + "text/latex": [ + "$553.85044 \\; \\mathrm{ct^{1/2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"SNR of TRAPPIST-1 in 10 s exposure:\")\n", + "howell_snr(counts_trappist1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can get a more detailed estimate of the signal to noise ratio if you include additional noise sources, such as the readnoise of the detector or the level of background noise to expect:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SNR of TRAPPIST-1 in 10 s:\n" + ] + }, + { + "data": { + "text/latex": [ + "$495.79257 \\; \\mathrm{ct^{1/2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "npix = 50 * u.pixel # number of pixels the object fills\n", + "n_background = 100 * u.pixel # number of pixels in background aperture\n", + "background = 1000 * u.ct / u.pixel # background noise\n", + "readnoise = 3.7 * u.ct / u.pixel # readnoise of ARCTIC from website\n", + "\n", + "print(\"SNR of TRAPPIST-1 in 10 s:\")\n", + "howell_snr(counts_trappist1, npix=npix,\n", + " n_background=n_background,\n", + " background=background,\n", + " readnoise=readnoise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These specifications can also be made to estimate the exposure time needed to obtain a given SNR (note that you must give the rate of the backround noise):" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exposure time needed to obtain SNR > 100:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python3.5/site-packages/astropy/units/quantity.py:464: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "//anaconda/lib/python3.5/site-packages/scipy/optimize/minpack.py:162: RuntimeWarning: The iteration is not making good progress, as measured by the \n", + " improvement from the last ten iterations.\n", + " warnings.warn(msg, RuntimeWarning)\n" + ] + }, + { + "data": { + "text/latex": [ + "$0.39743843 \\; \\mathrm{s}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from synphot.observation import exptime_from_howell_snr\n", + "\n", + "background_rate = background / exptime\n", + "\n", + "print(\"Exposure time needed to obtain SNR > 100:\")\n", + "exptime_from_howell_snr(desired_snr, countrate,\n", + " npix=npix,\n", + " n_background=n_background,\n", + " background_rate=background_rate,\n", + " readnoise=readnoise)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 11. Predicting photon counts of the same stars, this time observed by a space-based telescope" ] }, { @@ -670,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -679,19 +942,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING: W42: /var/folders/jg/nxrb0cds7_d1w0z9ldk5b7f40000gn/T/tmprbak7z13:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" + "WARNING: W42: /var/folders/bn/d7v8bk_16cjg8qcm1gj03m6h0000gp/T/tmp40avx65c:2:0: W42: No XML namespace specified [astropy.io.votable.tree]\n" ] }, { "data": { - "image/png": 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\n", 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JsdH8fcpQ/njJYNbu2Mcjs9dw/8w8Vm8r9To0E6YsiUQIVeXztcWM7p1+2IyFJvyICJfl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\nIrnutjQR+Y+IrHN/prrbRUT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    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -715,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 22, "metadata": { "collapsed": true }, @@ -738,7 +999,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -770,7 +1031,7 @@ "metadata": {}, "source": [ "\n", - "## 11. Comparing simulated counts to observed counts" + "## 12. Comparing simulated counts to observed counts" ] }, { @@ -782,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -796,7 +1057,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -832,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -840,23 +1101,21 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 86, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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KgFdeeYXXXnuNSpUqJRcXgE6dOlGiRAl8fHyoWLEiEyZMwMvLi6lTp/Lqq69S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\nTZqkv//97xo5cqTMzHcsAEAevGcGOIZDhw7pllt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"text/plain": [ "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], From 5cca55bcc1fb7dacd66986c57c2bae845144b268 Mon Sep 17 00:00:00 2001 From: tcjansen Date: Mon, 22 Jul 2019 13:18:26 -0700 Subject: [PATCH 26/26] Edited the objectives --- observing_examples.ipynb | 83 ++++++++++++---------------------------- 1 file changed, 25 insertions(+), 58 deletions(-) diff --git a/observing_examples.ipynb b/observing_examples.ipynb index b3c216f..1d945d4 100644 --- a/observing_examples.ipynb +++ b/observing_examples.ipynb @@ -21,12 +21,13 @@ "source": [ "## Objectives\n", "
      \n", - "
    • Query data directly from other websites using `astropy.coordinates.Skycoord`, `astroquery.Gaia`, `astropy.io`, and `astropy.utils`
      • \n", - "
    • Construct a source spectrum using `synphot.SourceSpectrum`
      • \n", - "
    • Simulate bandpass throughput with `synphot.SpectralElement`
      • \n", - "
    • Model effects on the source spectrum such as atmospheric transmission and quantum efficiency with `synphot.SpectralElement`
      • \n", - "
    • Combine all of these effects into a simulated observation with `synphot.Observation`
      • \n", - "
    • Compute the expected count rate from this observation with `synphot`'s `countrate()` function\n", + "
    • Query data directly from other websites using astropy.coordinates.Skycoord, astroquery.Gaia, astropy.io, and astropy.utils
      • \n", + "
    • Construct a source spectrum using synphot.SourceSpectrum
      • \n", + "
    • Simulate bandpass throughput with synphot.SpectralElement
      • \n", + "
    • Model effects on the source spectrum such as atmospheric transmission and quantum efficiency with synphot.SpectralElement
      • \n", + "
    • Combine all of these effects into a simulated observation with synphot.Observation
      • \n", + "
    • Compute the expected count rate from this observation with synphot's countrate() function\n", + "
    • Get the expected signal to noise ratios with synphot.observation's howell_snr() and exptime_from_howell_snr()\n", "
    " ] }, @@ -94,9 +95,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -203,9 +202,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -291,7 +288,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -381,9 +377,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -421,9 +415,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -461,9 +453,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -542,9 +532,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -581,9 +569,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -623,9 +609,7 @@ { "cell_type": "code", "execution_count": 58, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -706,7 +690,6 @@ "cell_type": "code", "execution_count": 60, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -743,9 +726,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -778,9 +759,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -818,9 +797,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -866,9 +843,7 @@ { "cell_type": "code", "execution_count": 66, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -934,9 +909,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -1000,9 +973,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1058,9 +1029,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1094,9 +1063,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1148,7 +1115,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1162,7 +1129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.1" } }, "nbformat": 4,