diff --git a/astropy_helpers b/astropy_helpers index 14ca346..70b09af 160000 --- a/astropy_helpers +++ b/astropy_helpers @@ -1 +1 @@ -Subproject commit 14ca346b0da3e92e65bdc398cc8f726cbd7be57d +Subproject commit 70b09af7e4f6b8a4e409412a913ff2adb05c12b7 diff --git a/docs/nb/SimulatingeBOSS.ipynb b/docs/nb/SimulatingeBOSS.ipynb new file mode 100644 index 0000000..5690487 --- /dev/null +++ b/docs/nb/SimulatingeBOSS.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulating the eBOSS Instrument" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook goes through how to simulate eBOSS spectra for a given source model. The model used here is defined so that it has an AB magnitude of 20 in each SDSS filter, but can be changed in the eboss.yaml file." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import specsim.simulator\n", + "\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Simulate a source model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import speclite\n", + "import astropy.units as u" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdss = speclite.filters.load_filters('sdss2010-*')\n", + "speclite.filters.plot_filters(sdss, wavelength_limits=(3000, 11000))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=1\n", + "\n", + "\n", + "\n", + "\n", + "
sdss2010-usdss2010-gsdss2010-rsdss2010-isdss2010-z
float64float64float64float64float64
20.47797281138541320.1845411068982220.0492714438072620.1516099261930420.52966327832596
" + ], + "text/plain": [ + "\n", + " sdss2010-u sdss2010-g ... sdss2010-i sdss2010-z \n", + " float64 float64 ... float64 float64 \n", + "------------------ ----------------- ... ----------------- -----------------\n", + "20.477972811385413 20.18454110689822 ... 20.15160992619304 20.52966327832596" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def quad(x):\n", + " return(x ** 2 / 1e-16)\n", + "\n", + "wlen = np.arange(np.min(sdss[0].wavelength)-1, np.max(sdss[-1].wavelength)+1) * u.Angstrom\n", + "flux = quad(np.linspace(8e-17, 1e-17, len(wlen))) * u.erg / (u.cm**2 * u.s * u.Angstrom)\n", + "\n", + "sdss.get_ab_magnitudes(flux, wlen)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(wlen, flux, '.')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sourceAB = Table([wlen, flux], names=('WAVELENGTH', 'FLUX'))\n", + "sourceAB.write('/Users/belaabolfathi/Documents/repos/specsim/specsim/data/sdss/spectra/spec-ABmag20.0.dat', format='ascii', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compare specsim output in DESI and eBOSS modes\n", + "\n", + "##### Initialize each simulator" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "desi = specsim.simulator.Simulator('desi', num_fibers=1, camera_output=True)\n", + "eboss = specsim.simulator.Simulator('eboss', num_fibers=1, camera_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Get simulated output for each camera" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/belaabolfathi/Documents/repos/specsim/specsim/simulator.py:600: RuntimeWarning: invalid value encountered in less\n", + " random_state.poisson(mean_electrons) - mean_electrons +\n", + "/Users/belaabolfathi/Documents/repos/specsim/specsim/simulator.py:600: RuntimeWarning: invalid value encountered in greater\n", + " random_state.poisson(mean_electrons) - mean_electrons +\n" + ] + } + ], + "source": [ + "def get_camera_output(simulator, seed=123):\n", + " gen = np.random.RandomState(seed)\n", + " simulator.simulate()\n", + " simulator.generate_random_noise(gen)\n", + " sim = simulator.simulated\n", + " cam1 = simulator.camera_output[0]\n", + " cam2 = simulator.camera_output[1]\n", + " \n", + " return(simulator.camera_output)\n", + " \n", + "desi_cam1, desi_cam2, desi_cam3 = get_camera_output(desi)\n", + "eboss_cam1, eboss_cam2 = get_camera_output(eboss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Plot DESI and eBOSS simulated inverse variance" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax1 = plt.subplots(figsize=(10,7))\n", + "\n", + "ax2 = ax1.twinx()\n", + "ax1.plot(wlen, flux, 'g', label='Model flux') # 1e-17 erg/s/cm2/A\n", + "ax1.plot(desi_cam1['wavelength'], desi_cam1['flux_inverse_variance'] * 1e-34, 'b.-', ms=1, lw=0.1, label='DESI-g') # 1e34 A2 cm4 s2 / erg2\n", + "ax1.plot(desi_cam2['wavelength'], desi_cam2['flux_inverse_variance'] * 1e-34, 'r.-', ms=1, lw=0.1, label='DESI-r') # 1e34 A2 cm4 s2 / erg2\n", + "ax1.plot(desi_cam3['wavelength'], desi_cam3['flux_inverse_variance'] * 1e-34, 'y.-', ms=1, lw=0.1, label='DESI-z') # 1e34 A2 cm4 s2 / erg2\n", + "ax1.plot(eboss_cam1['wavelength'], eboss_cam1['flux_inverse_variance'] * 1e-34, 'c', label='eBOSS-b') # 1e34 A2 cm4 s2 / erg2\n", + "ax1.plot(eboss_cam2['wavelength'], eboss_cam2['flux_inverse_variance'] * 1e-34, 'm', label='eBOSS-r') # 1e34 A2 cm4 s2 / erg2\n", + "\n", + "ax1.legend(loc=2)\n", + "ax1.set_xlabel('Wavelength [Å]')\n", + "ax1.set_ylabel('Camera Inverse Variance [10-34 Å2 cm4 s2 / erg2]', color='k')\n", + "ax2.set_ylabel('Model Flux [10-17 erg/s/cm2/Å]', color='g')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:galsim]", + "language": "python", + "name": "conda-env-galsim-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/nb/eBOSSConfigData.ipynb b/docs/nb/eBOSSConfigData.ipynb new file mode 100644 index 0000000..517ce41 --- /dev/null +++ b/docs/nb/eBOSSConfigData.ipynb @@ -0,0 +1,1085 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate instrument data for eBOSS config file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import bossdata\n", + "import os\n", + "import fitsio\n", + "\n", + "\n", + "import astropy.units as u\n", + "import pandas as pd\n", + "\n", + "from astropy.io import fits\n", + "from astropy.table import Table, vstack" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Refer to http://bossdata.readthedocs.io/en/latest/index.html to learn more about the bossdata package." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.1dev\n" + ] + } + ], + "source": [ + "print(bossdata.__version__)\n", + "\n", + "finder = bossdata.path.Finder()\n", + "mirror = bossdata.remote.Manager()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Access spCFrame files to get data for specpsf:\n", + "\n", + "* Wavelength\n", + "* Row width/pixel size\n", + "* Wavelength dispersion/FWHM resolution\n", + "* Spatial resolution/n_eff" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_cframe_hdulist(plate, mjd, band, fiber, sequence_number=0):\n", + " \n", + " \"\"\"Prepare plan path to access spCFrame data for a specific spectrograph camera.\n", + " \n", + " The spCFrame files contain the flux-calibrated spectra for a single CCD.\n", + " For more information see: https://data.sdss.org/datamodel/files/BOSS_SPECTRO_REDUX/RUN2D/PLATE4/spCFrame.html.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " plate: int\n", + " Plate number for the observation.\n", + " mjd: int\n", + " MJD for the observation.\n", + " band: str\n", + " Specifies which camera to use. Must be 'blue' or 'red'.\n", + " fiber: int\n", + " Fiber number indicating which spectrograph to use. Must be in the range 1-1000.\n", + " sequence_number: int\n", + " Science exposure sequence number, counting from zero.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " \n", + " :class: `astropy.io.fits.HDUList`\n", + " FITS data files associated with individual exposures.\n", + " \"\"\"\n", + "\n", + " plan_path = finder.get_plate_plan_path(plate=plate, mjd=mjd, combined=True)\n", + " local_plan_path = local_plan_path = mirror.get(plan_path)\n", + " Plan = bossdata.plate.Plan(local_plan_path)\n", + " exp_name = Plan.get_exposure_name(sequence_number=sequence_number, band=band, fiber=fiber, ftype='spCFrame')\n", + " local_fits = mirror.get(os.path.join(plan_path.strip(os.path.basename(plan_path)), exp_name))\n", + " \n", + " return(fits.open(local_fits))\n", + "\n", + "\n", + "def get_all_cframe_data(plate, mjd, sequence_number=0):\n", + " \n", + " \"\"\"Access cFrame data for each one of the four spectrograph cameras.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " plate: int\n", + " Plate number for the observation.\n", + " mjd: int\n", + " MJD for the observation.\n", + " sequence_number: int\n", + " Science exposure sequence number, counting from zero.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " \n", + " :class: `astropy.io.fits.HDUList`\n", + " FITS data files associated with individual exposures for all four spectrograph cameras.\n", + " \"\"\"\n", + " \n", + " b1_hdulist = get_cframe_hdulist(plate=plate, mjd=mjd, band='blue', fiber=30, sequence_number=sequence_number)\n", + " b2_hdulist = get_cframe_hdulist(plate=plate, mjd=mjd, band='blue', fiber=600, sequence_number=sequence_number)\n", + " r1_hdulist = get_cframe_hdulist(plate=plate, mjd=mjd, band='red', fiber=30, sequence_number=sequence_number)\n", + " r2_hdulist = get_cframe_hdulist(plate=plate, mjd=mjd, band='red', fiber=600, sequence_number=sequence_number)\n", + " \n", + " return b1_hdulist, b2_hdulist, r1_hdulist, r2_hdulist" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_neff(plate, mjd):\n", + " \n", + " \"\"\"Get profile width data for all fibers for a given PLATE, MJD, for all four spectrograph cameras. \n", + " The data is accessed via the spFlat files. \n", + " For more information, see: https://data.sdss.org/datamodel/files/BOSS_SPECTRO_REDUX/RUN2D/PLATE4/spFlat.html.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " plate: int\n", + " Plate number for the observation.\n", + " mjd: int\n", + " MJD for the observation.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " A set of interpolating functions along each trace of a half plate. \n", + " \"\"\"\n", + " \n", + " remote_plate_spec_path = finder.get_plate_spec_path(plate=plate, mjd=mjd)\n", + " plate_spec_path = mirror.get(remote_plate_spec_path)\n", + " PlateFile = bossdata.plate.PlateFile(plate_spec_path) \n", + " \n", + " exp_nameb1 = PlateFile.exposures.get_exposure_name(exposure_index=1, camera='b1', \n", + " ftype='spFlat')\n", + " exp_nameb2 = PlateFile.exposures.get_exposure_name(exposure_index=1, camera='b2', \n", + " ftype='spFlat')\n", + " exp_namer1 = PlateFile.exposures.get_exposure_name(exposure_index=1, camera='r1', \n", + " ftype='spFlat')\n", + " exp_namer2 = PlateFile.exposures.get_exposure_name(exposure_index=1, camera='r2', \n", + " ftype='spFlat')\n", + "\n", + " remote_plate_path_b1 = finder.get_plate_path(plate=plate, filename=exp_nameb1)\n", + " remote_plate_path_b2 = finder.get_plate_path(plate=plate, filename=exp_nameb2)\n", + " remote_plate_path_r1 = finder.get_plate_path(plate=plate, filename=exp_namer1)\n", + " remote_plate_path_r2 = finder.get_plate_path(plate=plate, filename=exp_namer2)\n", + " \n", + " plate_path_b1 = mirror.get(remote_plate_path_b1)\n", + " plate_path_b2 = mirror.get(remote_plate_path_b2)\n", + " plate_path_r1 = mirror.get(remote_plate_path_r1)\n", + " plate_path_r2 = mirror.get(remote_plate_path_r2)\n", + "\n", + " hdulist_b1 = fitsio.FITS(plate_path_b1, mode=fitsio.READONLY)\n", + " hdulist_b2 = fitsio.FITS(plate_path_b2, mode=fitsio.READONLY)\n", + " hdulist_r1 = fitsio.FITS(plate_path_r1, mode=fitsio.READONLY)\n", + " hdulist_r2 = fitsio.FITS(plate_path_r2, mode=fitsio.READONLY)\n", + " \n", + " TraceSet_b1 = bossdata.plate.TraceSet(hdulist_b1[3])\n", + " TraceSet_b2 = bossdata.plate.TraceSet(hdulist_b2[3])\n", + " TraceSet_r1 = bossdata.plate.TraceSet(hdulist_r1[3])\n", + " TraceSet_r2 = bossdata.plate.TraceSet(hdulist_r2[3])\n", + "\n", + " return TraceSet_b1, TraceSet_b2, TraceSet_r1, TraceSet_r2" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def combine(plate, mjd, fwhm=True):\n", + " \n", + " \"\"\"Combine all specpsf data for a given PLATE, MJD.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " plate: list or np.array\n", + " Plate numbers for the observations.\n", + " mjd: list or np.array\n", + " MJDs for the observations.\n", + " fwhm: \n", + " Specify whether to calculate rms or fwhm resolution.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " The wavelength, resolution, row width and spatial resolution data for all fibers on all four spectrograph \n", + " cameras on the specified plates. All have shape (len(plate),500,4128), except for the blue camera spatial\n", + " resolution array, which has shape (len(plate,500,4112)).\n", + " \n", + " \"\"\"\n", + " \n", + " fwhm = np.sqrt(8*np.log(2))\n", + " coef = 2*np.sqrt(np.pi)\n", + " \n", + " \n", + " # Profile width data (note: length is different for blue and red cameras)\n", + " b1neff = np.empty((len(plate), 500, 4112), dtype=float)\n", + " b2neff = np.empty((len(plate), 500, 4112), dtype=float)\n", + " r1neff = np.empty((len(plate), 500, 4128), dtype=float)\n", + " r2neff = np.empty((len(plate), 500, 4128), dtype=float) \n", + "\n", + " # Wavelength data\n", + " b1wlen = np.empty((len(plate), 500, 4128), dtype=float)\n", + " b2wlen = np.empty((len(plate), 500, 4128), dtype=float)\n", + " r1wlen = np.empty((len(plate), 500, 4128), dtype=float)\n", + " r2wlen = np.empty((len(plate), 500, 4128), dtype=float)\n", + "\n", + " # Wavelength dispersion data\n", + " b1res = np.empty((len(plate), 500, 4128), dtype=float)\n", + " b2res = np.empty((len(plate), 500, 4128), dtype=float)\n", + " r1res = np.empty((len(plate), 500, 4128), dtype=float)\n", + " r2res = np.empty((len(plate), 500, 4128), dtype=float)\n", + "\n", + " # Row width data\n", + " b1width = np.empty((len(plate), 500, 4128), dtype=float)\n", + " b2width = np.empty((len(plate), 500, 4128), dtype=float)\n", + " r1width = np.empty((len(plate), 500, 4128), dtype=float)\n", + " r2width = np.empty((len(plate), 500, 4128), dtype=float)\n", + " \n", + " \n", + " \n", + " for i in range(len(plate)):\n", + " \n", + " b1TraceSet, b2TraceSet, r1TraceSet, r2TraceSet = get_neff(plate=plate[i], mjd=mjd[i])\n", + "\n", + " b1neff[i] = b1TraceSet.get_y()\n", + " b2neff[i] = b2TraceSet.get_y()\n", + " r1neff[i] = r1TraceSet.get_y()\n", + " r2neff[i] = r2TraceSet.get_y()\n", + " \n", + " b1, b2, r1, r2 = get_all_cframe_data(plate=plate[i], mjd=mjd[i])\n", + " \n", + " b1wlen[i] = 10**b1[3].data\n", + " b2wlen[i] = 10**b2[3].data\n", + " r1wlen[i] = 10**r1[3].data\n", + " r2wlen[i] = 10**r2[3].data\n", + " \n", + " b1width[i] = np.gradient(b1wlen[i], axis=1)\n", + " b2width[i] = np.gradient(b2wlen[i], axis=1)\n", + " r1width[i] = np.gradient(r1wlen[i], axis=1)\n", + " r2width[i] = np.gradient(r2wlen[i], axis=1)\n", + "\n", + " b1res[i] = b1[4].data*0.0001*np.log(10)*b1wlen[i]\n", + " b2res[i] = b2[4].data*0.0001*np.log(10)*b2wlen[i]\n", + " r1res[i] = r1[4].data*0.0001*np.log(10)*r1wlen[i]\n", + " r2res[i] = r2[4].data*0.0001*np.log(10)*r2wlen[i]\n", + " \n", + " if fwhm:\n", + " \n", + " b1res[i] = b1res[i]*fwhm\n", + " b2res[i] = b2res[i]*fwhm\n", + " r1res[i] = r1res[i]*fwhm\n", + " r2res[i] = r2res[i]*fwhm\n", + " \n", + " \n", + " return(b1wlen, b2wlen, r1wlen, r2wlen, b1res, b2res, r1res, r2res, \n", + " b1width, b2width, r1width, r2width, coef*b1neff, coef*b2neff, coef*r1neff, coef*r2neff)\n", + "\n", + "\n", + "def define_canon_grid(bwlen, rwlen, n_out, lo_trim=None, hi_trim=None):\n", + " \n", + " \"\"\"Define a canonical wavelength grid based on the minimum and maximum wavlength range \n", + " of all fibers on a plate.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " bwlen: numpy.ndarray\n", + " An ndarray of shape (len(plate),1000,4128) of all fibers on b1 and b2 cameras. \n", + " rwlen: numpy.ndarray\n", + " An ndarray of shape (len(plate),1000,4128) of all 1000 fibers on r1 and r2 cameras.\n", + " n_out: int\n", + " The number of samples to generate for the canonical grid.\n", + " lo_trim: int\n", + " Lower index at which to trim the data. \n", + " hi_trim: int\n", + " Upper index at which to trim the data. \n", + " \n", + " Returns\n", + " -------\n", + " \n", + " Numpy arrays for the blue and red canonical wavelength grids.\n", + " \n", + " \"\"\"\n", + " \n", + " b_canon = np.linspace(np.max(bwlen[:,:,0]), np.min(bwlen[:,:,-1]), n_out)\n", + " r_canon = np.linspace(np.max(rwlen[:,:,0]), np.min(rwlen[:,:,-1]), n_out)\n", + " \n", + " if lo_trim:\n", + " b_canon = b_canon[lo_trim:]\n", + " r_canon = r_canon[lo_trim:]\n", + " \n", + " if hi_trim:\n", + " b_canon = b_canon[:hi_trim]\n", + " r_canon = r_canon[:hi_trim]\n", + " \n", + " return(b_canon, r_canon)\n", + "\n", + "def summarize(b_grid, r_grid, b_wlen, r_wlen, b_data, r_data, summary=np.median):\n", + " \n", + " \"\"\"Interpolate data over canonical grid and return the median over all fibers\n", + " on all plates.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " b_grid: np.array\n", + " The blue canonical wavelength grid.\n", + " r_grid: \n", + " The red canonical wavelength grid.\n", + " bwlen: numpy.ndarray\n", + " An ndarray of shape (len(plate),1000,4128) of all fibers on b1 and b2 cameras. \n", + " rwlen: numpy.ndarray\n", + " An ndarray of shape (len(plate),1000,4128) of all 1000 fibers on r1 and r2 cameras.\n", + " b_data: \n", + " An ndarray of blue camera data of shape (len(plate),1000,4128), except for the blue spatial \n", + " resolution array, which has shape (len(plate,500,4112)), to be interpolated over the blue canonical grid.\n", + " r_data: \n", + " An ndarray of red camera data of shape (len(plate),1000,4128) to be interpolated over the red canonical grid.\n", + " summary: \n", + " Statistic to use to calculate a single output for the camera data. \n", + " Returns\n", + " -------\n", + " \n", + " Numpy arrays of the camera data interpolated over the canonical grids. \n", + " \n", + " \"\"\"\n", + " \n", + " assert b_wlen.shape == b_data.shape\n", + " assert r_wlen.shape == r_data.shape\n", + " \n", + " n_out = len(b_canon) # red and blue have different lengths\n", + " num_plates = b_data.shape[0]\n", + " \n", + " b_interp = np.empty((num_plates, 1000, n_out), dtype=float)\n", + " r_interp = np.empty((num_plates, 1000, n_out), dtype=float)\n", + " \n", + " for plate in range(num_plates):\n", + " for fiber in range(1000):\n", + " b_interp[plate, fiber] = np.interp(x=b_grid, xp=b_wlen[plate][fiber], fp=b_data[plate][fiber])\n", + " r_interp[plate, fiber] = np.interp(x=r_grid, xp=r_wlen[plate][fiber], fp=r_data[plate][fiber])\n", + " \n", + " return(summary(b_interp, axis=(0,1)), summary(r_interp, axis=(0,1)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate config data using 20 different plate, mjds" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plate = [7027, 6963, 7301, 6759, 6002, 6178, 6626, 6882, 7389, 7453, \n", + " 7517, 6472, 6660, 6877, 6970, 6122, 7456, 7377, 7454, 7564]\n", + " \n", + "mjd = [56448, 56724, 56746, 56416, 56104, 56213, 56330, 56541, 56769, \n", + " 56749, 56772, 56362, 56370, 56544, 56444, 56246, 56727, 56741, 56751, 56804]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Get data for all plate-mjds\n", + "b1wlen, b2wlen, r1wlen, r2wlen, b1res, b2res, r1res, r2res, b1width, b2width, r1width, r2width, b1neff, b2neff, r1neff, r2neff = combine(plate, mjd, fwhm=True)\n", + "\n", + "# Combine data for blue and red cameras\n", + "bwlen = np.concatenate((b1wlen, b2wlen), axis=1)\n", + "rwlen = np.concatenate((r1wlen, r2wlen), axis=1)\n", + "bres = np.concatenate((b1res, b2res), axis=1)\n", + "rres = np.concatenate((r1res, r2res), axis=1)\n", + "bwidth = np.concatenate((b1width, b2width), axis=1)\n", + "rwidth = np.concatenate((r1width, r2width), axis=1)\n", + "bneff = np.concatenate((b1neff, b2neff), axis=1)\n", + "rneff = np.concatenate((r1neff, r2neff), axis=1)\n", + "\n", + "# Create canonical wavelength grid\n", + "n_out = bneff.shape[2]\n", + "b_canon, r_canon = define_canon_grid(bwlen, rwlen, n_out, lo_trim=60, hi_trim=-80)\n", + "\n", + "# Take the median\n", + "bres_int, rres_int = summarize(b_canon, r_canon, bwlen, rwlen, bres, rres)\n", + "bwid_int, rwid_int = summarize(b_canon, r_canon, bwlen, rwlen, bwidth, rwidth)\n", + "bneff_int, rneff_int = summarize(b_canon, r_canon, bwlen[:,:,:n_out], rwlen, bneff, rneff)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((3972,), (3972,), (3972,), (3972,))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bres_int.shape, rres_int.shape, bneff_int.shape, rneff_int.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compare to DESI instrument" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "desi_path = '/Users/belaabolfathi/Data/desimodel/0.4.2/data/specpsf/'\n", + "\n", + "desi_hdulist = fitsio.FITS(desi_path + 'psf-quicksim.fits', mode=fitsio.READONLY)\n", + "\n", + "quicksim_b, quicksim_r, quicksim_z = desi_hdulist[1].read(), desi_hdulist[2].read(), desi_hdulist[3].read()\n", + "\n", + "desi_bwlen = quicksim_b['wavelength']\n", + "desi_rwlen = quicksim_r['wavelength']\n", + "desi_zwlen = quicksim_z['wavelength']\n", + "\n", + "desi_bres = quicksim_b['fwhm_wave']\n", + "desi_rres = quicksim_r['fwhm_wave']\n", + "desi_zres = quicksim_z['fwhm_wave']\n", + "\n", + "desi_bwidth = quicksim_b['angstroms_per_row']\n", + "desi_rwidth = quicksim_r['angstroms_per_row']\n", + "desi_zwidth = quicksim_z['angstroms_per_row']\n", + "\n", + "desi_bneff = quicksim_b['fwhm_spatial']\n", + "desi_rneff = quicksim_r['fwhm_spatial']\n", + "desi_zneff = quicksim_z['fwhm_spatial']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Wavelength" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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jX8n777/P3Llz2bx5801NBpVS+dz27Vb/wI0brbWG9+yBihVdHZXKx0SEo0eP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\nA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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,6))\n", + "plt.plot(np.arange(0,len(desi_bwlen)), desi_bwlen, '--b', lw=1, label='DESI b')\n", + "plt.plot(np.arange(0,len(desi_rwlen)), desi_rwlen, '--r', lw=1, label='DESI r')\n", + "plt.plot(np.arange(0,len(desi_zwlen)), desi_zwlen, '--k', lw=1, label='DESI z')\n", + "plt.plot(b_canon, 'b', label='eBOSS b', lw=1)\n", + "plt.plot(r_canon, 'r', label='eBOSS r', lw=1)\n", + "plt.xlabel('Pixel', fontsize=14)\n", + "plt.ylabel('Wavelength (Angstrom)', fontsize=14)\n", + "plt.title('20 plates', fontsize=18)\n", + "plt.xlim(0,4850)\n", + "plt.legend(loc=2, fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Wavelength dispersion\n", + "\n", + "###### eBOSS" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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GvXN5nmwws70J97Lv5uE2P5FyR0PZRWS9ebg/rAthZnkBouHwX7DuTPQiIiL50JU/JrST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\ncZHYATgM2BG4r5j3KQB2Bc4AzgaOylj+IHHB6w8cAhybPC+0c/L3VKB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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14,5))\n", + "\n", + "ax1.plot(b_canon, bres_int, 'b')\n", + "ax2.plot(r_canon, rres_int, 'r')\n", + "\n", + "ax1.set_title('Blue side (20 plates)', fontsize=14)\n", + "ax1.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax1.set_ylabel('FWHM Resolution (Angstrom)', fontsize=14)\n", + "ax2.set_title('Red Side (20 plates)', fontsize=14)\n", + "ax2.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### DESI" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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D7+yE6dPzjcXMbAi4+OKLu7Y91ZrZyDRgBT17Q74MmFz9cIaW9vaeazMzK1I8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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(14,5))\n", + "\n", + "\n", + "ax1.scatter(desi_bwlen, desi_bres, s=1, c='b')\n", + "ax2.scatter(desi_rwlen, desi_rres, s=1, c='r')\n", + "ax3.scatter(desi_zwlen, desi_zres, s=1, c='k')\n", + "\n", + "ax1.set_title('Resolution (g)', fontsize=16)\n", + "ax1.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax1.set_ylabel('FWHM Resolution (Angstrom)', fontsize=14)\n", + "ax2.set_title('Resolution (r)', fontsize=16)\n", + "ax2.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax3.set_title('Resolution (z)', fontsize=16)\n", + "ax3.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Row width\n", + "\n", + "##### eBOSS" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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4GA55882xtu1dd6mKeylSYi4iBVqyBJ5+Onr5unSJm/wXXxzT34YOhaOPjnoi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\n0qwFEfEf4E1JG2VJOwIv4XOQl/35rFkt+DzYIvI1vUh8vTXtDWBbSct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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14,5))\n", + "\n", + "ax1.plot(b_canon, bwid_int, 'b')\n", + "ax2.plot(r_canon, rwid_int, 'r')\n", + "\n", + "ax1.set_title('Blue side (20 plates)', fontsize=14)\n", + "ax1.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax1.set_ylabel('Row width (Angstrom/Pixel)', fontsize=14)\n", + "ax2.set_title('Red Side (20 plates)', fontsize=14)\n", + "ax2.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### DESI" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YAMwCbgOazOzyNN8fd1/r7lPdfeqYMWPSfZmEurpg7drkeHe3tmQTkTyzfn3q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\n/ve0TY9J+pu0rqb7x27aVNOfqfcsImp6AQTsnz7uB9wLNAP/Bowpsf1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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(14,5))\n", + "\n", + "\n", + "ax1.scatter(desi_bwlen, desi_bwidth, s=1, c='b')\n", + "ax2.scatter(desi_rwlen, desi_rwidth, s=1, c='r')\n", + "ax3.scatter(desi_zwlen, desi_zwidth, s=1, c='k')\n", + "\n", + "ax1.set_title('Row width (g)', fontsize=16)\n", + "ax1.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax1.set_ylabel('Row width (Angstrom/Pixel)', fontsize=14)\n", + "ax2.set_title('Row width (r)', fontsize=16)\n", + "ax2.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax3.set_title('Row width (z)', fontsize=16)\n", + "ax3.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Spatial resolution\n", + "\n", + "##### eBOSS" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7wjaoNWrATTclnUZEREQAuOYa+PBDePll2HTTpNMUlLgN8w6EueYiIiIF7YMP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\ndUXVqjEFTdeucdNG05yJVDpKzKXSmzYtEvL+/ePGc5s2MfJ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GAJ3MrHZyl79Tsk7WgLv/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\niIiIiIiIpOj/AS1K1JBkgPkSAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14,5))\n", + "\n", + "ax1.plot(b_canon, bneff_int, 'b')\n", + "ax2.plot(r_canon, rneff_int, 'r')\n", + "\n", + "ax1.set_title('Blue side (20 plates)', fontsize=14)\n", + "ax1.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax1.set_ylabel('Spatial Resolution (Angstrom)', fontsize=14)\n", + "ax2.set_title('Red Side (20 plates)', fontsize=14)\n", + "ax2.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### DESI" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SUzaV/ENEJNYKTRI3FXjEzE4D3gC2Z77o7jOKHViUVGpNpPiuuirdPv306OKI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\nbHW4/jEz2xjGV29mJ4TlJ4brr4bbL4i8xzfD8kYzGxlPTdrkqFMlHKc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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(14,5))\n", + "\n", + "\n", + "ax1.scatter(desi_bwlen, desi_bneff, s=1, c='b')\n", + "ax2.scatter(desi_rwlen, desi_rneff, s=1, c='r')\n", + "ax3.scatter(desi_zwlen, desi_zneff, s=1, c='k')\n", + "\n", + "ax1.set_title('n_eff (g)', fontsize=16)\n", + "ax1.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax1.set_ylabel('n_eff (Angstrom)', fontsize=14)\n", + "ax2.set_title('n_eff (r)', fontsize=16)\n", + "ax2.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "ax3.set_title('n_eff (z)', fontsize=16)\n", + "ax3.set_xlabel('Wavelength (Angstrom)', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write specpsf data" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "base_path = '/Users/belaabolfathi/Documents/repos/specsim/specsim/data/sdss/'\n", + "\n", + "blue = Table([b_canon*u.Angstrom, bres_int*u.Angstrom, bwid_int*u.Angstrom/u.pix, \n", + " bneff_int*u.pix], names=('wavelength', 'fwhm_wave', 'angstroms_per_row', \n", + " 'neff_spatial'))\n", + "red = Table([r_canon*u.Angstrom, rres_int*u.Angstrom, rwid_int*u.Angstrom/u.pix, \n", + " rneff_int*u.pix], names=('wavelength', 'fwhm_wave', 'angstroms_per_row', \n", + " 'neff_spatial'))\n", + "\n", + "blue.write(base_path + 'specpsf/eboss-blue.fits', overwrite=True)\n", + "red.write(base_path + 'specpsf/eboss-red.fits', overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "base_path = '/Users/belaabolfathi/Documents/repos/specsim/specsim/data/sdss/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adjust additional conifg data to be within interpolation range" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def adjust_interp(data, wlen_min, wlen_max, col1, col2):\n", + " beg = np.arange(wlen_min, 3500, 0.1)\n", + " end = np.arange(10000, wlen_max, 0.1)\n", + " col_beg = np.full(len(beg), data[col2][0])\n", + " col_end = np.full(len(end), data[col2][-1])\n", + " pad_beg = Table([beg, col_beg], names=(col1, col2))\n", + " pad_end = Table([end, col_end], names=(col1, col2))\n", + " new_data = vstack([pad_beg, data])\n", + " new_data = vstack([new_data, pad_end])\n", + " return(new_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adjust sky model" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "spec_sky = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/spectra/spec-sky.dat', format='ascii')\n", + "spec_sky_grey = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/spectra/spec-sky-grey.dat', format='ascii')\n", + "spec_sky_bright = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/spectra/spec-sky-bright.dat', format='ascii')\n", + "\n", + "new_spec_sky = adjust_interp(data=spec_sky, wlen_min=2980, wlen_max=10920, col1='WAVELENGTH', col2='FLUX')\n", + "new_spec_sky_grey = adjust_interp(data=spec_sky_grey, wlen_min=2980, wlen_max=10920, col1='WAVELENGTH', col2='FLUX')\n", + "new_spec_sky_bright = adjust_interp(data=spec_sky_bright, wlen_min=2980, wlen_max=10920, col1='WAVELENGTH', col2='FLUX')\n", + "\n", + "new_spec_sky.write(base_path + 'spectra/eboss-spec-sky.dat', format='ascii', overwrite=True)\n", + "new_spec_sky_grey.write(base_path + 'spectra/eboss-spec-sky-grey.dat', format='ascii', overwrite=True)\n", + "new_spec_sky_bright.write(base_path + 'spectra/eboss-spec-sky-bright.dat', format='ascii', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adjust extinction" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "extinction = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/spectra/ZenithExtinction-KPNO.dat', format='ascii')\n", + "new_ext = adjust_interp(data=extinction, wlen_min=2980, wlen_max=10920, col1='WAVELENGTH', col2='EXTINCTION')\n", + "\n", + "new_ext.write(base_path + 'spectra/eboss-KPNO-extinction.dat', format='ascii', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Adjust fiberloss (for fiberloss method = table)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "qso_fiberloss = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/throughput/fiberloss-qso.dat', format='ascii')\n", + "elg_fiberloss = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/throughput/fiberloss-elg.dat', format='ascii')\n", + "lrg_fiberloss = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/throughput/fiberloss-lrg.dat', format='ascii')\n", + "star_fiberloss = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/throughput/fiberloss-star.dat', format='ascii')\n", + "sky_fiberloss = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/throughput/fiberloss-sky.dat', format='ascii')\n", + "perf_fiberloss = Table.read('/Users/belaabolfathi/Data/desimodel/0.4.2/data/throughput/fiberloss-perfect.dat', format='ascii')\n", + "\n", + "new_qso_fiberloss = adjust_interp(data=qso_fiberloss, wlen_min=2980, wlen_max=10920, col1='col1', col2='col2')\n", + "new_elg_fiberloss = adjust_interp(data=elg_fiberloss, wlen_min=2980, wlen_max=10920, col1='col1', col2='col2')\n", + "new_lrg_fiberloss = adjust_interp(data=lrg_fiberloss, wlen_min=2980, wlen_max=10920, col1='col1', col2='col2')\n", + "new_star_fiberloss = adjust_interp(data=star_fiberloss, wlen_min=2980, wlen_max=10920, col1='col1', col2='col2')\n", + "new_sky_fiberloss = adjust_interp(data=sky_fiberloss, wlen_min=2980, wlen_max=10920, col1='col1', col2='col2')\n", + "new_perf_fiberloss = adjust_interp(data=perf_fiberloss, wlen_min=2980, wlen_max=10920, col1='col1', col2='col2')\n", + "\n", + "new_qso_fiberloss.write(base_path + 'throughput/eboss-fiberloss-qso.dat', format='ascii', overwrite=True)\n", + "new_elg_fiberloss.write(base_path + 'throughput/eboss-fiberloss-elg.dat', format='ascii', overwrite=True)\n", + "new_lrg_fiberloss.write(base_path + 'throughput/eboss-fiberloss-lrg.dat', format='ascii', overwrite=True)\n", + "new_star_fiberloss.write(base_path + 'throughput/eboss-fiberloss-star.dat', format='ascii', overwrite=True)\n", + "new_sky_fiberloss.write(base_path + 'throughput/eboss-fiberloss-sky.dat', format='ascii', overwrite=True)\n", + "new_perf_fiberloss.write(base_path + 'throughput/eboss-fiberloss-perfect.dat', format='ascii', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write throughput data\n", + "\n", + "##### use throughput from Smee et al. paper (see Fig. 28 of https://arxiv.org/abs/1208.2233)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "boss_throughput_curves = pd.read_excel(base_path + 'throughput/throughputs_for_BOSS_paper_revA.xlsx', sheetname='BOSS')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "other_losses = 0.85 * 0.96 * 0.83\n", + "\n", + "throughput_wlen = boss_throughput_curves['Wavelength']\n", + "blue_thru = boss_throughput_curves['Telescope'] * boss_throughput_curves['Collimator'] * boss_throughput_curves['Dichroic R'] * boss_throughput_curves['Blue grism'] * boss_throughput_curves['Blue camera'] * boss_throughput_curves['e2v CCD'] * other_losses\n", + "red_thru = boss_throughput_curves['Telescope'] * boss_throughput_curves['Collimator'] * boss_throughput_curves['Dichroic T'] * boss_throughput_curves['Red grism'] * boss_throughput_curves['Red camera'] * boss_throughput_curves['LBL CCD'] * other_losses" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,5))\n", + "plt.plot(throughput_wlen * 10, blue_thru, c='b', ls=':')\n", + "plt.plot(throughput_wlen * 10, red_thru, c='r', ls=':')\n", + "plt.xlabel('Wavelength (Angstrom)')\n", + "plt.ylabel('Throughput')\n", + "plt.xlim(3000,11000)\n", + "plt.ylim(0,1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "eboss_thru_b = Table([throughput_wlen * 10, blue_thru], names=('wavelength', 'throughput'))\n", + "eboss_thru_r = Table([throughput_wlen * 10, red_thru], names=('wavelength', 'throughput'))\n", + "\n", + "eboss_thru_b.write(base_path + 'throughput/smee-eboss-thru-b.fits', overwrite=True)\n", + "eboss_thru_r.write(base_path + 'throughput/smee-eboss-thru-r.fits', overwrite=True)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda env:bossdata]", + "language": "python", + "name": "conda-env-bossdata-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/specsim/data/config/.swp b/specsim/data/config/.swp new file mode 100644 index 0000000..96f7e88 Binary files /dev/null and b/specsim/data/config/.swp differ diff --git a/specsim/data/config/desi.yaml b/specsim/data/config/desi.yaml index cf50df9..754edc4 100644 --- a/specsim/data/config/desi.yaml +++ b/specsim/data/config/desi.yaml @@ -143,7 +143,7 @@ instrument: columns: wavelength: { index: 0, unit: Angstrom } fiber_acceptance: { index: 1 } - # Fits file of precomputed galsim fiber acceptance + # Fits file of precomputed galsim fiber acceptance # the fits file is created with specsim/fitgalsim.py # the fits file is read with specsim/fastfiberacceptance.py fast_fiber_acceptance_path: throughput/galsim-fiber-acceptance.fits diff --git a/specsim/data/config/eboss.yaml b/specsim/data/config/eboss.yaml new file mode 100644 index 0000000..22c8b88 --- /dev/null +++ b/specsim/data/config/eboss.yaml @@ -0,0 +1,313 @@ +name: eBOSS QuickSim + +# The base path is pre-pended to all non-absolute path values below. +# {...} will be expanded using environment variables. +base_path: + +# Be verbose during the simulation? +verbose: no + +# Specify the wavelength grid to use for simulation. For comparison, the +# table below summarizes the ranges of non-zero throughput in each camera, +# wavelengths that can disperse into CCDs (with a 5-sigma cut), and the +# extents of each CCD's active pixels covered by all fibers (as defined by +# cameras.*.ccd.table below). Note that individual fibers will generally have +# slightly more coverage, so we are simulating the worst-case coverage of +# all fibers. +# +# Range b(min) b(max) r(min) r(max) z(min) z(max) +# Angstrom Angstrom Angstrom Angstrom Angstrom Angstrom +# ---------- -------- -------- -------- -------- -------- -------- +# Throughput 3533.000 5998.000 5564.000 7805.000 7360.000 9913.000 +# Simulation 3550.000 ................................... 9850.000 +# Response 3565.400 5952.600 5621.700 7744.300 7431.000 9838.000 +# CCD 3569.000 5949.000 5625.000 7741.000 7435.000 9834.000 +wavelength_grid: + unit: Angstrom + min: 3000.0 + max: 10890.0 + step: 0.1 + +# The atmosphere configuration is interpreted and validated by the +# specsim.atmosphere module. +atmosphere: + # Sky emission surface brightness. + sky: + table: + # The .dat extension is not automatically recognized as ascii. + format: ascii + columns: + wavelength: { index: 0, unit: Angstrom } + surface_brightness: + index: 1 + # Note the factor of 1e-17 in the units! + unit: 1e-17 erg / (Angstrom arcsec2 cm2 s) + paths: + # Each path defines a possible condition. + dark: sdss/spectra/eboss-spec-sky.dat + #grey: sdss/spectra/eboss-spec-sky-grey.dat + #bright: sdss/spectra/eboss-spec-sky-bright.dat + # Specify the current condition. + condition: dark + # Atmospheric seeing (only used when instrument.fiberloss.method = galsim) + seeing: + # The seeing is assumed to scale with wavelength as + # fwhm(wlen) = fwhm_ref * (wlen / wlen_ref) ** -0.2 + fwhm_ref: 1.1 arcsec + wlen_ref: 6355 Angstrom + # The seeing PSF is modeled as a Moffat profile. + moffat_beta: 3.5 + # Surface brightness of scattered moonlight. + moon: + # Un-normalized spectrum of scattered moonlight. + # We use the Wehrli 1985 extraterrestial solar spectrum here. + # Seee http://rredc.nrel.gov/solar/spectra/am0/ for details. + table: + columns: + wavelength: { index: 1, unit: Angstrom } + flux: + index: 2 + # The actual units are W / (m2 micron) but we lie here + # (by a factor of 10) since the input normalization does + # not matter and old versions of speclite.filters do + # not interpret the actual units correctly. + unit: erg / (cm2 s Angstrom) + path: sdss/sky/solarspec.txt + format: ascii.basic + constants: + # Phase of the moon from 0 (full) to 1 (new). + moon_phase: 0.5 + # Zenith angles of the moon. An angle > 90 (below the horizon) + # will zero the scattered moon contribution. + moon_zenith: 100 deg + # Separation angle between the observation and moon. + separation_angle: 60 deg + # Zenith extinction coefficients. + extinction: + table: + format: ascii + columns: + wavelength: { index: 0, unit: Angstrom } + extinction_coefficient: { index: 1 } + path: sdss/spectra/eboss-KPNO-extinction.dat + # Should extinction be applied to the sky emission? + extinct_emission: yes + # Airmass to use when the pointing is not calculated from the source + # and observation parameters. The focal_x and focal_y parameters of + # source.location.constants must be set in order for the airmass + # specified here to be used. + airmass: 1.0 + +# The instrument configuration is interpreted and validated by the +# specsim.instrument module. +instrument: + name: eBOSS + constants: + primary_mirror_diameter: 2.5 m + obscuration_diameter: 0.625 m + support_width: 0.025 m + fiber_diameter: 120.0 um + field_radius: 325.0 mm + plate_scale: + constants: + value: 60.45 um / arcsec + fiberloss: + # Method for calculating fiberloss fractions. + # Either galsim or table. + method: galsim + # Number of wavelengths where fiberloss should be calculated + # and interpolated. Ignored unless method is galsim. + num_wlen: 11 + # Number of pixels used to subdivide the fiber diameter for + # numerical convolution and integration calculations with galsim. + num_pixels: 16 + # Table of pre-tabulated fiberloss fractions vs wavelength for + # different source types. Ignored unless method is table. + table: + format: ascii + paths: + # Each path corresponds to a different source type. + # These 'eboss' fiberlosses are desi values, + # adjusted to lie within interpolation range of + # wavelength grid + qso: sdss/throughput/eboss-fiberloss-qso.dat + elg: sdss/throughput/eboss-fiberloss-elg.dat + lrg: sdss/throughput/eboss-fiberloss-lrg.dat + star: sdss/throughput/eboss-fiberloss-star.dat + sky: sdss/throughput/eboss-fiberloss-sky.dat + perfect: sdss/throughput/eboss-fiberloss-perfect.dat + columns: + wavelength: { index: 0, unit: Angstrom } + fiber_acceptance: { index: 1 } + blur: + # Read RMS blur values tabulated as a function of field angle and + # wavelength, and derived from ray tracing simulations. For details, + # see $DESIMODEL/doc/nb/DESI-0347_Throughput.ipynb. + path: sdss/throughput/DESI-0347_blur.ecsv + # The ECSV format is not auto-detected so we specify it explicitly. + format: ascii.ecsv + offset: + # Read radial centroid offsets tabulated as a function of field angle + # and wavelength, and derived from ray tracing simulations. For + # details, see $DESIMODEL/doc/nb/DESI-0347_Throughput.ipynb. + path: sdss/throughput/DESI-0347_offset.ecsv + # The ECSV format is not auto-detected so we specify it explicitly. + format: ascii.ecsv + # Read random achromatic centroid offsets from a FITS file. + # Uncomment any one of these lines to select a different random set. + # No random offsets are applied if all lines are commented out. + #random: sdss/throughput/DESI-0347_random_offset_1.fits + #random: sdss/throughput/DESI-0347_random_offset_2.fits + #random: sdss/throughput/DESI-0347_random_offset_3.fits + cameras: + b: + constants: + # These values are copied from desimodel/data/desi.yaml + read_noise: 2.0 electron/pixel**2 + # We treat "pixel" as a linear unit, so we need pixel**2 here. + dark_current: 2.1 electron/(hour pixel**2) + gain: 1.02 electron/adu + # Clip the resolution matrix at this number of sigmas. + num_sigmas_clip: 5 + # Output pixels are used to report simulation results but are + # not the physical pixels. The output pixel size must be a + # multiple of wavelength_grid.step. + output_pixel_size: 0.5 Angstrom + ccd: + table: + path: sdss/specpsf/eboss-blue.fits + extrapolated_value: 0.0 + columns: + wavelength: { name: wavelength } + row_size: + name: angstroms_per_row + # The file uses Angstrom / row but "row" is not + # recognized by astropy.units so we replace it with + # the linear "pixel" unit. + unit: Angstrom / pixel + override_unit: yes + fwhm_resolution: { name: fwhm_wave } + neff_spatial: { name: neff_spatial } + throughput: + table: + path: sdss/throughput/smee-eboss-thru-b.fits + extrapolated_value: 0.0 + columns: + wavelength: { name: wavelength, unit: Angstrom } + throughput: { name: throughput } + r: + constants: + # These values are copied from desimodel/data/desi.yaml + read_noise: 2.875 electron/pixel**2 + # We treat "pixel" as a linear unit, so we need pixel**2 here. + dark_current: 4.25 electron/(hour pixel**2) + gain: 1.66 electron/adu + # Clip the resolution matrix at this number of sigmas. + num_sigmas_clip: 5 + # Output pixels are used to report simulation results but are + # not the physical pixels. The output pixel size must be a + # multiple of wavelength_grid.step. + output_pixel_size: 0.5 Angstrom + ccd: + table: + path: sdss/specpsf/eboss-red.fits + extrapolated_value: 0.0 + columns: + wavelength: { name: wavelength } + row_size: + name: angstroms_per_row + # The file uses Angstrom / row but "row" is not + # recognized by astropy.units so we replace it with + # the linear "pixel" unit. + unit: Angstrom / pixel + override_unit: yes + fwhm_resolution: { name: fwhm_wave } + neff_spatial: { name: neff_spatial } + throughput: + table: + path: sdss/throughput/smee-eboss-thru-r.fits + extrapolated_value: 0.0 + columns: + wavelength: { name: wavelength, unit: Angstrom } + throughput: { name: throughput } + + +# The source configuration is interpreted and validated by the +# specsim.source module. +source: + name: 20th AB magnitude reference + # The type is only used when instrument.fiberloss.method is 'table'. + type: qso + table: + format: ascii + path: sdss/spectra/spec-ABmag20.0.dat + columns: + wavelength: { index: 0, unit: Angstrom } + flux: + index: 1 + # Note the factor of 1e-17 in the units! + unit: 1e-17 erg / (Angstrom cm2 s) + # Transverse profile of the source on the sky, used to calculate the + # fiberloss fraction when instrument.fiberloss.method = galsim (but + # ignored otherwise). + profile: + # The sum of disk + bulge fractions must be <= 1. If it is < 1, + # the remainder is point-like, i.e., star or QSO. + disk_fraction: 0.0 + bulge_fraction: 0.0 + disk_shape: + half_light_radius: 0.8 arcsec + position_angle: 45 deg + minor_major_axis_ratio: 0.5 + bulge_shape: + half_light_radius: 1.2 arcsec + position_angle: 60 deg + minor_major_axis_ratio: 0.8 + # Location of the source on the sky. A source will not be visible if + # it lies out the observation field of view. + location: + # If focal-plane (x,y) coordinates are not specified, they will be + # calculated from the sky coordinates and observing conditions + # and the corresponding airmass will be used for the atmosphere model. + constants: + # Comment out these lines to have (x,y) and the airmass + # calculated automatically for this source and observation. + focal_x: -70.7 mm + focal_y: +70.7 mm + # Sky coordinates are optional (and ignored) when focal-plane (x,y) + # are specified. + sky: { coordinates: 0h 0d, frame: icrs } + # Set these parameters to apply a redshift transformation. + z_in: + z_out: + # Set these parameters to normalize in a specified filter. + filter_name: + ab_magnitude_out: + +# The observation configuration is interpreted and validated by the +# specsim.observation module. +observation: + observatory: KPNO + constants: + exposure_time: 900.84 s + # Atmospheric pressure at the telescope (not at sea level) used + # to calculate atmospheric refraction. Leave commented out to use a + # nominal value calculated for the observatory elevation. + #pressure: 79 kPa + # Air temperature at the telescope used to calculate atmospheric + # refraction (but only has a small effect). + temperature: 15 deg_C + # Relative humidity (0-1) at the telescope used to calculate atmospheric + # refraction (but only has a small effect). + relative_humidity: 0. + exposure_start: + timestamp: + when: 55000.5 + format: mjd + # This optional parameter adjusts the timestamp by +/-12h to + # achieve the specified hour angle for the boresight (ra, dec). + #adjust_to_hour_angle: -0.5h + pointing: + sky: + coordinates: 0h 0d + frame: icrs diff --git a/specsim/data/sdss/sky/solarspec.txt b/specsim/data/sdss/sky/solarspec.txt new file mode 100644 index 0000000..12ee4c7 --- /dev/null +++ b/specsim/data/sdss/sky/solarspec.txt @@ -0,0 +1,668 @@ +199.5 1995 5 +200.5 2005 7 +201.5 2015 7 +202.5 2025 8 +203.5 2035 9 +204.5 2045 9 +205.5 2055 10 +206.5 2065 10 +207.5 2075 11 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a/specsim/data/sdss/spectra/.DS_Store b/specsim/data/sdss/spectra/.DS_Store new file mode 100644 index 0000000..ebbc76c Binary files /dev/null and b/specsim/data/sdss/spectra/.DS_Store differ diff --git a/specsim/data/sdss/spectra/eboss-KPNO-extinction.dat b/specsim/data/sdss/spectra/eboss-KPNO-extinction.dat new file mode 100644 index 0000000..a757ed5 --- /dev/null +++ b/specsim/data/sdss/spectra/eboss-KPNO-extinction.dat @@ -0,0 +1,79402 @@ +# ------------ ------------- +WAVELENGTH EXTINCTION +2980.0 0.6 +2980.1 0.6 +2980.2 0.6 +2980.2999999999997 0.6 +2980.3999999999996 0.6 +2980.4999999999995 0.6 +2980.5999999999995 0.6 +2980.6999999999994 0.6 +2980.7999999999993 0.6 +2980.899999999999 0.6 +2980.999999999999 0.6 +2981.099999999999 0.6 +2981.199999999999 0.6 +2981.299999999999 0.6 +2981.3999999999987 0.6 +2981.4999999999986 0.6 +2981.5999999999985 0.6 +2981.6999999999985 0.6 +2981.7999999999984 0.6 +2981.8999999999983 0.6 +2981.999999999998 0.6 +2982.099999999998 0.6 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3722.5 +"" 18.84863 3722.6 +"" 19.02128 3722.7 +"" 19.12311 3722.8 +"" 19.07664 3722.9 +"" 18.99466 3723.0 +"" 18.98574 3723.1 +"" 19.18933 3723.2 +"" 19.64866 3723.3 +"" 19.88929 3723.4 +"" 19.62053 3723.5 +"" 19.34256 3723.6 +"" 19.26149 3723.7 +"" 19.38692 3723.8 +"" 19.61765 3723.9 +"" 19.60308 3724.0 +"" 19.50971 3724.1 +"" 19.56715 3724.2 +"" 19.55218 3724.3 +"" 19.42081 3724.4 +"" 19.31004 3724.5 +"" 19.33467 3724.6 +"" 19.38331 3724.7 +"" 19.31654 3724.8 +"" 19.19857 3724.9 +"" 19.06121 3725.0 +"" 19.00035 3725.1 +"" 19.07677 3725.2 +"" 19.1592 3725.3 +"" 19.12304 3725.4 +"" 19.07537 3725.5 +"" 19.0399 3725.6 +"" 18.99874 3725.7 +"" 19.02057 3725.8 +"" 19.0442 3725.9 +"" 19.10284 3726.0 +"" 19.37137 3726.1 +"" 19.7368 3726.2 +"" 19.82184 3726.3 +"" 19.64267 3726.4 +"" 19.5116 3726.5 +"" 19.46264 3726.6 +"" 19.49277 3726.7 +"" 19.641 3726.8 +"" 19.85624 3726.9 +"" 20.19227 3727.0 +"" 20.59741 3727.1 +"" 20.75434 3727.2 +"" 20.57898 3727.3 +"" 20.29382 3727.4 +"" 20.00185 3727.5 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11.22433 5010.5 +"" 11.17383 5010.6 +"" 11.16874 5010.7 +"" 11.17504 5010.8 +"" 11.18624 5010.9 +"" 11.16764 5011.0 +"" 11.13095 5011.1 +"" 11.11005 5011.2 +"" 11.09175 5011.3 +"" 11.07986 5011.4 +"" 11.11516 5011.5 +"" 11.16996 5011.6 +"" 11.17576 5011.7 +"" 11.18247 5011.8 +"" 11.20317 5011.9 +"" 11.16657 5012.0 +"" 11.10537 5012.1 +"" 11.07588 5012.2 +"" 11.07118 5012.3 +"" 11.09258 5012.4 +"" 11.15429 5012.5 +"" 11.19349 5012.6 +"" 11.15249 5012.7 +"" 11.0893 5012.8 +"" 11.06 5012.9 +"" 11.0325 5013.0 +"" 11.0054 5013.1 +"" 11.02501 5013.2 +"" 11.05031 5013.3 +"" 11.04422 5013.4 +"" 11.03682 5013.5 +"" 11.03282 5013.6 +"" 11.00632 5013.7 +"" 10.97083 5013.8 +"" 10.93998 5013.9 +"" 10.94703 5014.0 +"" 10.99684 5014.1 +"" 11.05504 5014.2 +"" 11.09715 5014.3 +"" 11.10335 5014.4 +"" 11.10295 5014.5 +"" 11.09035 5014.6 +"" 11.05476 5014.7 +"" 11.03086 5014.8 +"" 11.03547 5014.9 +"" 11.04837 5015.0 +"" 11.02947 5015.1 +"" 10.98118 5015.2 +"" 10.9358 5015.3 +"" 10.9038 5015.4 +"" 10.89822 5015.5 +"" 10.91796 5015.6 +"" 10.925 5015.7 +"" 10.90538 5015.8 +"" 10.90354 5015.9 +"" 10.95101 5016.0 +"" 11.01481 5016.1 +"" 11.03451 5016.2 +"" 11.01022 5016.3 +"" 10.99282 5016.4 +"" 10.97883 5016.5 +"" 10.98293 5016.6 +"" 11.04443 5016.7 +"" 11.14964 5016.8 +"" 11.24784 5016.9 +"" 11.28505 5017.0 +"" 11.23055 5017.1 +"" 11.13365 5017.2 +"" 11.06846 5017.3 +"" 10.99476 5017.4 +"" 10.91208 5017.5 +"" 10.89607 5017.6 +"" 10.92716 5017.7 +"" 10.96288 5017.8 +"" 11.04868 5017.9 +"" 11.16849 5018.0 +"" 11.21289 5018.1 +"" 11.1474 5018.2 +"" 11.0676 5018.3 +"" 11.06021 5018.4 +"" 11.11561 5018.5 +"" 11.14031 5018.6 +"" 11.07682 5018.7 +"" 10.97162 5018.8 +"" 10.89285 5018.9 +"" 10.87704 5019.0 +"" 10.8966 5019.1 +"" 10.93354 5019.2 +"" 10.96995 5019.3 +"" 10.98105 5019.4 +"" 10.95025 5019.5 +"" 10.86108 5019.6 +"" 10.76939 5019.7 +"" 10.74342 5019.8 +"" 10.7752 5019.9 +"" 10.82273 5020.0 +"" 10.88349 5020.1 +"" 10.92079 5020.2 +"" 10.93909 5020.3 +"" 10.9726 5020.4 +"" 11.017 5020.5 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10.87572 5025.6 +"" 10.8913 5025.7 +"" 10.94009 5025.8 +"" 11.0035 5025.9 +"" 11.01731 5026.0 +"" 10.97151 5026.1 +"" 10.93702 5026.2 +"" 10.91852 5026.3 +"" 10.90403 5026.4 +"" 10.92344 5026.5 +"" 10.94154 5026.6 +"" 10.88945 5026.7 +"" 10.82454 5026.8 +"" 10.79742 5026.9 +"" 10.78245 5027.0 +"" 10.82058 5027.1 +"" 10.92228 5027.2 +"" 10.99219 5027.3 +"" 10.96139 5027.4 +"" 10.86663 5027.5 +"" 10.80493 5027.6 +"" 10.81242 5027.7 +"" 10.84911 5027.8 +"" 10.89433 5027.9 +"" 10.92063 5028.0 +"" 10.91864 5028.1 +"" 10.89875 5028.2 +"" 10.85771 5028.3 +"" 10.80806 5028.4 +"" 10.78148 5028.5 +"" 10.7682 5028.6 +"" 10.74662 5028.7 +"" 10.77092 5028.8 +"" 10.859 5028.9 +"" 10.9189 5029.0 +"" 10.89591 5029.1 +"" 10.81945 5029.2 +"" 10.73814 5029.3 +"" 10.69995 5029.4 +"" 10.69889 5029.5 +"" 10.72871 5029.6 +"" 10.77501 5029.7 +"" 10.78579 5029.8 +"" 10.75428 5029.9 +"" 10.73802 5030.0 +"" 10.7666 5030.1 +"" 10.82523 5030.2 +"" 10.91039 5030.3 +"" 10.9847 5030.4 +"" 10.99531 5030.5 +"" 10.97111 5030.6 +"" 10.96222 5030.7 +"" 10.94803 5030.8 +"" 10.91804 5030.9 +"" 10.89344 5031.0 +"" 10.86755 5031.1 +"" 10.8586 5031.2 +"" 10.87423 5031.3 +"" 10.85072 5031.4 +"" 10.81575 5031.5 +"" 10.85395 5031.6 +"" 10.914 5031.7 +"" 10.9709 5031.8 +"" 11.01951 5031.9 +"" 11.00592 5032.0 +"" 10.96143 5032.1 +"" 10.92143 5032.2 +"" 10.88164 5032.3 +"" 10.84424 5032.4 +"" 10.82955 5032.5 +"" 10.85313 5032.6 +"" 10.88457 5032.7 +"" 10.88148 5032.8 +"" 10.87459 5032.9 +"" 10.8891 5033.0 +"" 10.9197 5033.1 +"" 10.95461 5033.2 +"" 10.94832 5033.3 +"" 10.90553 5033.4 +"" 10.85898 5033.5 +"" 10.82734 5033.6 +"" 10.8276 5033.7 +"" 10.8346 5033.8 +"" 10.86657 5033.9 +"" 10.91017 5034.0 +"" 10.91948 5034.1 +"" 10.94409 5034.2 +"" 10.993 5034.3 +"" 11.00921 5034.4 +"" 10.98271 5034.5 +"" 10.93912 5034.6 +"" 10.93193 5034.7 +"" 10.97454 5034.8 +"" 11.01685 5034.9 +"" 11.02686 5035.0 +"" 10.99407 5035.1 +"" 10.94937 5035.2 +"" 10.93158 5035.3 +"" 10.94229 5035.4 +"" 10.9589 5035.5 +"" 10.95021 5035.6 +"" 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5045.7 +"" 10.78311 5045.8 +"" 10.819 5045.9 +"" 10.86381 5046.0 +"" 10.89392 5046.1 +"" 10.92063 5046.2 +"" 10.92414 5046.3 +"" 10.88445 5046.4 +"" 10.86596 5046.5 +"" 10.89897 5046.6 +"" 10.92738 5046.7 +"" 10.9213 5046.8 +"" 10.89451 5046.9 +"" 10.89412 5047.0 +"" 10.94873 5047.1 +"" 10.97654 5047.2 +"" 10.92575 5047.3 +"" 10.88286 5047.4 +"" 10.90867 5047.5 +"" 10.96538 5047.6 +"" 10.99269 5047.7 +"" 10.95701 5047.8 +"" 10.91512 5047.9 +"" 10.92363 5048.0 +"" 10.95464 5048.1 +"" 10.97435 5048.2 +"" 10.96976 5048.3 +"" 10.92288 5048.4 +"" 10.88539 5048.5 +"" 10.9177 5048.6 +"" 10.97011 5048.7 +"" 10.96142 5048.8 +"" 10.91933 5048.9 +"" 10.92244 5049.0 +"" 10.94426 5049.1 +"" 10.92097 5049.2 +"" 10.86858 5049.3 +"" 10.82809 5049.4 +"" 10.8231 5049.5 +"" 10.84261 5049.6 +"" 10.85102 5049.7 +"" 10.83664 5049.8 +"" 10.81945 5049.9 +"" 10.82626 5050.0 +"" 10.83227 5050.1 +"" 10.81848 5050.2 +"" 10.8209 5050.3 +"" 10.85751 5050.4 +"" 10.90042 5050.5 +"" 10.91753 5050.6 +"" 10.89284 5050.7 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145.2501 8770.4 +"" 82.22806 8770.5 +"" 29.97499 8770.6 +"" 9.270711 8770.7 +"" 4.836537 8770.8 +"" 4.276463 8770.9 +"" 4.203579 8771.0 +"" 4.130195 8771.1 +"" 4.127861 8771.2 +"" 4.579166 8771.3 +"" 5.960092 8771.4 +"" 7.737119 8771.5 +"" 8.106645 8771.6 +"" 6.61917 8771.7 +"" 4.951496 8771.8 +"" 4.148682 8771.9 +"" 3.914928 8772.0 +"" 3.866314 8772.1 +"" 3.882019 8772.2 +"" 3.901456 8772.3 +"" 3.956321 8772.4 +"" 4.311378 8772.5 +"" 5.311904 8772.6 +"" 6.705229 8772.7 +"" 7.251956 8772.8 +"" 6.315681 8772.9 +"" 4.978408 8773.0 +"" 4.247424 8773.1 +"" 4.003969 8773.2 +"" 3.911426 8773.3 +"" 3.881471 8773.4 +"" 3.890268 8773.5 +"" 3.951283 8773.6 +"" 4.039749 8773.7 +"" 4.048225 8773.8 +"" 3.98697 8773.9 +"" 3.944917 8774.0 +"" 3.949343 8774.1 +"" 3.958059 8774.2 +"" 3.950995 8774.3 +"" 3.979881 8774.4 +"" 4.027857 8774.5 +"" 4.012163 8774.6 +"" 3.943009 8774.7 +"" 3.864745 8774.8 +"" 3.806211 8774.9 +"" 3.796037 8775.0 +"" 3.841833 8775.1 +"" 3.904549 8775.2 +"" 3.941325 8775.3 +"" 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10.01464 8780.4 +"" 34.69466 8780.5 +"" 96.37759 8780.6 +"" 164.9325 8780.7 +"" 169.3024 8780.8 +"" 105.3524 8780.9 +"" 40.69829 8781.0 +"" 11.77752 8781.1 +"" 5.141443 8781.2 +"" 4.27117 8781.3 +"" 4.122396 8781.4 +"" 4.050072 8781.5 +"" 4.052509 8781.6 +"" 4.089504 8781.7 +"" 4.083501 8781.8 +"" 4.022647 8781.9 +"" 3.973263 8782.0 +"" 3.97843 8782.1 +"" 4.012775 8782.2 +"" 4.017931 8782.3 +"" 3.964988 8782.4 +"" 3.923394 8782.5 +"" 3.93197 8782.6 +"" 3.910916 8782.7 +"" 3.852593 8782.8 +"" 3.816858 8782.9 +"" 3.795675 8783.0 +"" 3.799341 8783.1 +"" 3.851627 8783.2 +"" 3.897344 8783.3 +"" 3.938729 8783.4 +"" 4.057116 8783.5 +"" 4.170003 8783.6 +"" 4.127168 8783.7 +"" 3.983505 8783.8 +"" 3.866381 8783.9 +"" 3.795857 8784.0 +"" 3.774384 8784.1 +"" 3.7996 8784.2 +"" 3.827466 8784.3 +"" 3.842772 8784.4 +"" 3.875078 8784.5 +"" 3.909625 8784.6 +"" 3.91245 8784.7 +"" 3.898407 8784.8 +"" 3.881723 8784.9 +"" 3.863099 8785.0 +"" 3.859486 8785.1 +"" 3.875742 8785.2 +"" 3.881299 8785.3 +"" 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8845.4 +"" 3.886699 8845.5 +"" 3.865107 8845.6 +"" 3.849755 8845.7 +"" 3.850363 8845.8 +"" 3.85914 8845.9 +"" 3.868518 8846.0 +"" 3.885926 8846.1 +"" 3.889074 8846.2 +"" 3.855172 8846.3 +"" 3.809579 8846.4 +"" 3.810997 8846.5 +"" 3.867995 8846.6 +"" 3.932973 8846.7 +"" 3.936971 8846.8 +"" 3.872118 8846.9 +"" 3.811176 8847.0 +"" 3.812624 8847.1 +"" 3.855972 8847.2 +"" 3.907119 8847.3 +"" 4.101107 8847.4 +"" 4.763335 8847.5 +"" 5.862063 8847.6 +"" 6.55189 8847.7 +"" 6.392418 8847.8 +"" 6.301645 8847.9 +"" 6.642774 8848.0 +"" 6.408202 8848.1 +"" 5.339629 8848.2 +"" 4.363357 8848.3 +"" 3.955785 8848.4 +"" 3.872653 8848.5 +"" 3.883651 8848.6 +"" 3.911418 8848.7 +"" 3.932747 8848.8 +"" 3.932494 8848.9 +"" 3.897732 8849.0 +"" 3.908881 8849.1 +"" 4.102728 8849.2 +"" 4.519036 8849.3 +"" 4.880163 8849.4 +"" 4.810492 8849.5 +"" 4.39582 8849.6 +"" 4.035947 8849.7 +"" 3.899205 8849.8 +"" 3.868273 8849.9 +"" 3.837671 8850.0 +"" 3.802888 8850.1 +"" 3.780907 8850.2 +"" 3.790055 8850.3 +"" 3.822472 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133.3022 8905.6 +"" 90.85713 8905.7 +"" 40.44006 8905.8 +"" 13.94499 8905.9 +"" 5.963116 8906.0 +"" 4.314945 8906.1 +"" 4.002494 8906.2 +"" 3.915783 8906.3 +"" 3.937392 8906.4 +"" 4.134172 8906.5 +"" 4.732191 8906.6 +"" 5.71632 8906.7 +"" 6.25335 8906.8 +"" 5.700278 8906.9 +"" 4.694908 8907.0 +"" 4.079278 8907.1 +"" 3.857757 8907.2 +"" 3.772046 8907.3 +"" 3.715045 8907.4 +"" 3.668454 8907.5 +"" 3.671214 8907.6 +"" 3.688673 8907.7 +"" 3.726282 8907.8 +"" 3.902941 8907.9 +"" 4.214601 8908.0 +"" 4.44663 8908.1 +"" 4.387559 8908.2 +"" 4.113629 8908.3 +"" 3.873127 8908.4 +"" 3.804857 8908.5 +"" 3.845307 8908.6 +"" 3.861735 8908.7 +"" 3.851645 8908.8 +"" 3.864464 8908.9 +"" 3.847874 8909.0 +"" 3.773283 8909.1 +"" 3.710312 8909.2 +"" 3.725862 8909.3 +"" 3.78419 8909.4 +"" 3.81933 8909.5 +"" 3.80789 8909.6 +"" 3.754779 8909.7 +"" 3.722638 8909.8 +"" 3.724097 8909.9 +"" 3.738137 8910.0 +"" 3.720196 8910.1 +"" 3.650675 8910.2 +"" 3.609685 8910.3 +"" 3.642574 8910.4 +"" 3.817943 8910.5 +"" 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176.7074 9521.9 +"" 170.3373 9522.0 +"" 188.2372 9522.1 +"" 178.1172 9522.2 +"" 115.3871 9522.3 +"" 49.21006 9522.4 +"" 16.08301 9522.5 +"" 6.748151 9522.6 +"" 4.796892 9522.7 +"" 4.194934 9522.8 +"" 3.881776 9522.9 +"" 3.713368 9523.0 +"" 3.657341 9523.1 +"" 3.705583 9523.2 +"" 3.813145 9523.3 +"" 3.864287 9523.4 +"" 3.796229 9523.5 +"" 3.677842 9523.6 +"" 3.574864 9523.7 +"" 3.480105 9523.8 +"" 3.427447 9523.9 +"" 3.46041 9524.0 +"" 3.495262 9524.1 +"" 3.470544 9524.2 +"" 3.413636 9524.3 +"" 3.320078 9524.4 +"" 3.226411 9524.5 +"" 3.175783 9524.6 +"" 3.147405 9524.7 +"" 3.130247 9524.8 +"" 3.129639 9524.9 +"" 3.156561 9525.0 +"" 3.198263 9525.1 +"" 3.219605 9525.2 +"" 3.257537 9525.3 +"" 3.315659 9525.4 +"" 3.350801 9525.5 +"" 3.382484 9525.6 +"" 3.389146 9525.7 +"" 3.380758 9525.8 +"" 3.44575 9525.9 +"" 3.581872 9526.0 +"" 3.703015 9526.1 +"" 3.752717 9526.2 +"" 3.710739 9526.3 +"" 3.588411 9526.4 +"" 3.513023 9526.5 +"" 3.603596 9526.6 +"" 3.710878 9526.7 +"" 3.64747 9526.8 +"" 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9541.9 +"" 3.380781 9542.0 +"" 3.360494 9542.1 +"" 3.331266 9542.2 +"" 3.303039 9542.3 +"" 3.291941 9542.4 +"" 3.309293 9542.5 +"" 3.308686 9542.6 +"" 3.237788 9542.7 +"" 3.153521 9542.8 +"" 3.159263 9542.9 +"" 3.236286 9543.0 +"" 3.302098 9543.1 +"" 3.34687 9543.2 +"" 3.331594 9543.3 +"" 3.217705 9543.4 +"" 3.114108 9543.5 +"" 3.109521 9543.6 +"" 3.172993 9543.7 +"" 3.267305 9543.8 +"" 3.344748 9543.9 +"" 3.350241 9544.0 +"" 3.321583 9544.1 +"" 3.311245 9544.2 +"" 3.301098 9544.3 +"" 3.29382 9544.4 +"" 3.333063 9544.5 +"" 3.373736 9544.6 +"" 3.347838 9544.7 +"" 3.287 9544.8 +"" 3.254673 9544.9 +"" 3.264236 9545.0 +"" 3.295778 9545.1 +"" 3.343471 9545.2 +"" 3.422283 9545.3 +"" 3.499085 9545.4 +"" 3.483418 9545.5 +"" 3.385951 9545.6 +"" 3.321693 9545.7 +"" 3.308416 9545.8 +"" 3.291328 9545.9 +"" 3.26467 9546.0 +"" 3.215714 9546.1 +"" 3.179115 9546.2 +"" 3.170958 9546.3 +"" 3.129861 9546.4 +"" 3.065653 9546.5 +"" 3.027356 9546.6 +"" 3.017578 9546.7 +"" 3.019891 9546.8 +"" 3.032333 9546.9 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3.218636 9552.0 +"" 3.246688 9552.1 +"" 3.22746 9552.2 +"" 3.216183 9552.3 +"" 3.266895 9552.4 +"" 3.331858 9552.5 +"" 3.350711 9552.6 +"" 3.336924 9552.7 +"" 3.292376 9552.8 +"" 3.258429 9552.9 +"" 3.268141 9553.0 +"" 3.278885 9553.1 +"" 3.282526 9553.2 +"" 3.31718 9553.3 +"" 3.388562 9553.4 +"" 3.426475 9553.5 +"" 3.398358 9553.6 +"" 3.34996 9553.7 +"" 3.340413 9553.8 +"" 3.373535 9553.9 +"" 3.371868 9554.0 +"" 3.349881 9554.1 +"" 3.413613 9554.2 +"" 3.516477 9554.3 +"" 3.594508 9554.4 +"" 3.744071 9554.5 +"" 4.092614 9554.6 +"" 5.149557 9554.7 +"" 9.781599 9554.8 +"" 27.00234 9554.9 +"" 65.45029 9555.0 +"" 108.0682 9555.1 +"" 116.5182 9555.2 +"" 82.55811 9555.3 +"" 39.48506 9555.4 +"" 14.557 9555.5 +"" 6.304041 9555.6 +"" 4.406683 9555.7 +"" 3.862426 9555.8 +"" 3.549149 9555.9 +"" 3.307812 9556.0 +"" 3.154645 9556.1 +"" 3.108337 9556.2 +"" 3.14376 9556.3 +"" 3.214153 9556.4 +"" 3.270275 9556.5 +"" 3.300368 9556.6 +"" 3.3334 9556.7 +"" 3.359624 9556.8 +"" 3.309516 9556.9 +"" 3.215799 9557.0 +"" 3.181562 9557.1 +"" 3.196194 9557.2 +"" 3.228378 9557.3 +"" 3.285509 9557.4 +"" 3.307133 9557.5 +"" 3.279995 9557.6 +"" 3.267718 9557.7 +"" 3.28002 9557.8 +"" 3.302823 9557.9 +"" 3.349386 9558.0 +"" 3.407419 9558.1 +"" 3.431602 9558.2 +"" 3.410604 9558.3 +"" 3.350987 9558.4 +"" 3.28476 9558.5 +"" 3.240583 9558.6 +"" 3.249926 9558.7 +"" 3.363088 9558.8 +"" 3.477491 9558.9 +"" 3.457414 9559.0 +"" 3.360427 9559.1 +"" 3.288279 9559.2 +"" 3.286512 9559.3 +"" 3.277884 9559.4 +"" 3.228898 9559.5 +"" 3.22715 9559.6 +"" 3.243173 9559.7 +"" 3.190956 9559.8 +"" 3.107338 9559.9 +"" 3.074762 9560.0 +"" 3.086534 9560.1 +"" 3.110137 9560.2 +"" 3.143049 9560.3 +"" 3.209782 9560.4 +"" 3.302685 9560.5 +"" 3.349048 9560.6 +"" 3.299031 9560.7 +"" 3.231344 9560.8 +"" 3.252996 9560.9 +"" 3.313929 9561.0 +"" 3.343162 9561.1 +"" 3.357635 9561.2 +"" 3.381868 9561.3 +"" 3.36028 9561.4 +"" 3.299113 9561.5 +"" 3.267336 9561.6 +"" 3.268609 9561.7 +"" 3.288742 9561.8 +"" 3.316514 9561.9 +"" 3.356767 9562.0 +"" 3.39307 9562.1 +"" 3.404533 9562.2 +"" 3.422706 9562.3 +"" 3.577998 9562.4 +"" 4.187391 9562.5 +"" 5.608634 9562.6 +"" 7.344277 9562.7 +"" 8.20162 9562.8 +"" 8.293962 9562.9 +"" 8.395306 9563.0 +"" 7.833648 9563.1 +"" 6.156091 9563.2 +"" 4.514534 9563.3 +"" 3.729217 9563.4 +"" 3.467649 9563.5 +"" 3.360413 9563.6 +"" 3.349905 9563.7 +"" 3.416178 9563.8 +"" 3.476571 9563.9 +"" 3.457114 9564.0 +"" 3.394737 9564.1 +"" 3.388899 9564.2 +"" 3.428032 9564.3 +"" 3.430364 9564.4 +"" 3.414358 9564.5 +"" 3.403431 9564.6 +"" 3.361774 9564.7 +"" 3.336667 9564.8 +"" 3.324249 9564.9 +"" 3.278342 9565.0 +"" 3.235905 9565.1 +"" 3.193878 9565.2 +"" 3.160701 9565.3 +"" 3.167914 9565.4 +"" 3.187486 9565.5 +"" 3.19883 9565.6 +"" 3.221303 9565.7 +"" 3.242175 9565.8 +"" 3.261688 9565.9 +"" 3.290981 9566.0 +"" 3.285104 9566.1 +"" 3.244486 9566.2 +"" 3.21881 9566.3 +"" 3.230332 9566.4 +"" 3.269356 9566.5 +"" 3.267699 9566.6 +"" 3.221421 9566.7 +"" 3.258474 9566.8 +"" 3.327717 9566.9 +"" 3.30118 9567.0 +"" 3.264013 9567.1 +"" 3.305246 9567.2 +"" 3.357189 9567.3 +"" 3.331331 9567.4 +"" 3.232144 9567.5 +"" 3.132708 9567.6 +"" 3.11161 9567.7 +"" 3.207363 9567.8 +"" 3.364046 9567.9 +"" 3.449079 9568.0 +"" 3.450983 9568.1 +"" 3.475384 9568.2 +"" 3.508068 9568.3 +"" 3.513031 9568.4 +"" 3.542224 9568.5 +"" 3.578296 9568.6 +"" 3.565799 9568.7 +"" 3.517632 9568.8 +"" 3.478065 9568.9 +"" 3.533638 9569.0 +"" 3.729111 9569.1 +"" 4.075764 9569.2 +"" 5.304107 9569.3 +"" 11.61235 9569.4 +"" 35.41799 9569.5 +"" 84.48393 9569.6 +"" 127.7999 9569.7 +"" 121.9398 9569.8 +"" 80.47977 9569.9 +"" 61.27671 9570.0 +"" 91.33065 9570.1 +"" 129.5296 9570.2 +"" 120.9595 9570.3 +"" 71.80447 9570.4 +"" 28.43742 9570.5 +"" 9.949665 9570.6 +"" 5.311008 9570.7 +"" 4.341851 9570.8 +"" 4.034994 9570.9 +"" 3.834337 9571.0 +"" 3.699821 9571.1 +"" 3.561403 9571.2 +"" 3.403636 9571.3 +"" 3.367489 9571.4 +"" 3.495102 9571.5 +"" 3.872596 9571.6 +"" 4.587439 9571.7 +"" 5.166781 9571.8 +"" 4.999624 9571.9 +"" 4.283467 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3624.5 +"" 4.186346 3624.6 +"" 3.948115 3624.7 +"" 3.788984 3624.8 +"" 3.705752 3624.9 +"" 3.824721 3625.0 +"" 4.33909 3625.1 +"" 4.774059 3625.2 +"" 4.527627 3625.3 +"" 4.156797 3625.4 +"" 3.935865 3625.5 +"" 3.665034 3625.6 +"" 3.485003 3625.7 +"" 3.538972 3625.8 +"" 3.653741 3625.9 +"" 3.70771 3626.0 +"" 3.877979 3626.1 +"" 3.942848 3626.2 +"" 3.922917 3626.3 +"" 4.091486 3626.4 +"" 4.205355 3626.5 +"" 4.263824 3626.6 +"" 4.337892 3626.7 +"" 4.480961 3626.8 +"" 4.70983 3626.9 +"" 4.5892 3627.0 +"" 4.253868 3627.1 +"" 4.104537 3627.2 +"" 3.948906 3627.3 +"" 3.750976 3627.4 +"" 3.657145 3627.5 +"" 3.649614 3627.6 +"" 3.751482 3627.7 +"" 3.817252 3627.8 +"" 4.035621 3627.9 +"" 4.56829 3628.0 +"" 4.887159 3628.1 +"" 4.669628 3628.2 +"" 4.094197 3628.3 +"" 3.708766 3628.4 +"" 3.724136 3628.5 +"" 3.857805 3628.6 +"" 4.233774 3628.7 +"" 4.804543 3628.8 +"" 4.650613 3628.9 +"" 3.955581 3629.0 +"" 3.796051 3629.1 +"" 4.00992 3629.2 +"" 3.994589 3629.3 +"" 3.792859 3629.4 +"" 3.591128 3629.5 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b/specsim/data/sdss/throughput/DESI-0347_blur.ecsv new file mode 100644 index 0000000..c8a1e2d --- /dev/null +++ b/specsim/data/sdss/throughput/DESI-0347_blur.ecsv @@ -0,0 +1,50 @@ +# %ECSV 0.9 +# --- +# datatype: +# - {name: wavelength, unit: Angstrom, datatype: float64, format: '{:.1f}', description: Wavelength} +# - {name: r=0.00 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=0.00 deg.} +# - {name: r=0.15 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=0.15 deg.} +# - {name: r=0.30 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=0.30 deg.} +# - {name: r=0.45 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=0.45 deg.} +# - {name: r=0.60 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=0.60 deg.} +# - {name: r=0.75 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=0.75 deg.} +# - {name: r=0.90 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=0.90 deg.} +# - {name: r=1.05 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=1.05 deg.} +# - {name: r=1.20 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=1.20 deg.} +# - {name: r=1.35 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=1.35 deg.} +# - {name: r=1.50 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=1.50 deg.} +# - {name: r=1.60 deg, unit: micron, datatype: float64, format: '{:.4f}', description: RMS +# spot size at field angle r=1.60 deg.} +# meta: !!omap +# - {description: 'RMS spot size as a function of wavelength and field angle derived +# from DESI-0347-v10. Chromatic contributions are based on a text file prepared +# by Mike Sholl that is also plotted in the geometric_blur tab of the throughput +# spreadsheet in DESI-0347-v10. Achromatic contributions are taken from cells B14:B26 +# added in quadrature.'} +wavelength "r=0.00 deg" "r=0.15 deg" "r=0.30 deg" "r=0.45 deg" "r=0.60 deg" "r=0.75 deg" "r=0.90 deg" "r=1.05 deg" "r=1.20 deg" "r=1.35 deg" "r=1.50 deg" "r=1.60 deg" +3550.0 22.7735 21.7600 19.2640 16.6192 14.9810 14.2602 13.6255 12.5408 11.7269 14.6014 22.5561 19.2047 +3675.0 20.3559 19.5103 17.5191 15.6129 14.5913 14.1237 13.5305 12.5613 12.0402 14.9283 21.9891 18.0148 +3800.0 18.0112 17.3337 15.8389 14.6402 14.2084 13.9882 13.4360 12.5819 12.3654 15.2598 21.4253 16.8523 +4125.0 14.1816 13.9022 13.5095 13.6381 14.0235 13.9194 13.1796 12.3527 12.5529 15.4999 20.2628 14.7976 +4500.0 12.2831 12.3482 12.7821 13.6445 14.2458 13.9232 12.8591 11.9291 12.2957 15.2612 19.3235 13.8125 +5000.0 11.6955 12.0070 12.9289 14.0535 14.5254 13.8398 12.4350 11.3497 11.6064 14.3592 18.1450 13.4968 +5500.0 11.8068 12.1956 13.2268 14.3156 14.6009 13.7121 12.2520 11.2444 11.3404 13.6110 17.1664 13.4026 +6000.0 11.9455 12.3647 13.4231 14.4581 14.6404 13.7276 12.4600 11.7610 11.8125 13.4884 16.6143 13.4975 +6500.0 12.0634 12.5021 13.5776 14.5813 14.7240 13.8827 12.9111 12.5982 12.7086 13.8611 16.4479 13.7934 +7000.0 12.1785 12.6353 13.7301 14.7178 14.8447 14.0930 13.4086 13.4543 13.6754 14.4474 16.5086 14.2057 +7500.0 12.3155 12.7895 13.9036 14.8760 14.9796 14.2858 13.8227 14.1614 14.5114 15.0357 16.6506 14.6475 +8000.0 12.5058 12.9944 14.1211 15.0663 15.1190 14.4245 14.0944 14.6536 15.1346 15.5144 16.7806 15.0669 +8500.0 12.7875 13.2855 14.4117 15.3083 15.2706 14.5035 14.2107 14.9204 15.5283 15.8412 16.8536 15.4470 +9400.0 13.3797 13.8803 14.9824 15.7816 15.5669 14.5946 14.1968 14.9874 15.7409 16.0608 16.8952 15.9709 +9850.0 13.6853 14.1857 15.2729 16.0211 15.7163 14.6403 14.1898 15.0209 15.8478 16.1712 16.9160 16.2361 diff --git a/specsim/data/sdss/throughput/DESI-0347_offset.ecsv b/specsim/data/sdss/throughput/DESI-0347_offset.ecsv new file mode 100644 index 0000000..69b7f45 --- /dev/null +++ b/specsim/data/sdss/throughput/DESI-0347_offset.ecsv @@ -0,0 +1,51 @@ +# %ECSV 0.9 +# --- +# datatype: +# - {name: wavelength, unit: Angstrom, datatype: float64, format: '{:.1f}', description: Center +# of wavelength band} +# - {name: r=0.00 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=0.00 deg.} +# - {name: r=0.15 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=0.15 deg.} +# - {name: r=0.30 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=0.30 deg.} +# - {name: r=0.45 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=0.45 deg.} +# - {name: r=0.60 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=0.60 deg.} +# - {name: r=0.75 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=0.75 deg.} +# - {name: r=0.90 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=0.90 deg.} +# - {name: r=1.05 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=1.05 deg.} +# - {name: r=1.20 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=1.20 deg.} +# - {name: r=1.35 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=1.35 deg.} +# - {name: r=1.50 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=1.50 deg.} +# - {name: r=1.60 deg, unit: micron, datatype: float64, format: '{:.4f}', description: Radial +# centroid offset at field angle r=1.60 deg.} +# meta: !!omap +# - {description: 'Radial centroid offset as a function of wavelength and field angle +# derived from DESI-0347-v10. Values are based on a text file prepared by Mike Sholl +# that is also plotted in the geometric_blur tab of the throughput spreadsheet in +# DESI-0347-v10. Values are derived from ray tracing of the ideal optics and do +# not include the random achromatic errors in cells B43:B72 of the throughput sheet.'} +wavelength "r=0.00 deg" "r=0.15 deg" "r=0.30 deg" "r=0.45 deg" "r=0.60 deg" "r=0.75 deg" "r=0.90 deg" "r=1.05 deg" "r=1.20 deg" "r=1.35 deg" "r=1.50 deg" "r=1.60 deg" +3550.0 -0.0000 -6.8780 -12.6879 -16.4800 -17.5370 -15.4780 -10.3850 -2.9980 4.9010 9.6610 3.9220 -10.1910 +3675.0 -0.0000 -6.0190 -11.0867 -14.3560 -15.1860 -13.2310 -8.5540 -1.7960 5.4870 10.0970 5.5870 -6.5080 +3800.0 0.0000 -5.1600 -9.4855 -12.2320 -12.8350 -10.9840 -6.7230 -0.5940 6.0730 10.5330 7.2520 -2.8250 +4125.0 -0.0000 -3.4122 -6.2499 -7.9990 -8.2640 -6.8200 -3.6770 0.8290 5.8440 9.5920 8.4770 2.4710 +4500.0 0.0000 -1.9561 -3.5813 -4.5770 -4.7130 -3.8460 -1.9640 0.7720 3.9250 6.5720 6.8380 4.1590 +5000.0 -0.0000 -0.5098 -0.9577 -1.2850 -1.4430 -1.3900 -1.0970 -0.5490 0.2480 1.2620 2.4100 2.9210 +5500.0 -0.0000 0.4864 0.8341 0.9220 0.6600 0.0140 -0.9880 -2.2110 -3.3630 -3.8550 -2.5130 0.0100 +6000.0 0.0000 1.2761 2.2580 2.6850 2.3610 1.1850 -0.8140 -3.4190 -6.1180 -7.8640 -6.5530 -2.6350 +6500.0 0.0000 1.9770 3.5357 4.3040 3.9970 2.4640 -0.2700 -3.9250 -7.8220 -10.5590 -9.3130 -4.3430 +7000.0 0.0000 2.6496 4.7785 5.9230 5.7250 3.9930 0.7450 -3.6880 -8.4930 -11.9820 -10.7550 -4.9320 +7500.0 0.0000 3.3256 6.0442 7.6140 7.6170 5.8220 2.2460 -2.7410 -8.2140 -12.2500 -10.9680 -4.4080 +8000.0 -0.0000 4.0228 7.3637 9.4140 9.7020 7.9650 4.2160 -1.1410 -7.0860 -11.4920 -10.0710 -2.8440 +8500.0 -0.0000 4.7508 8.7536 11.3400 11.9910 10.4180 6.6260 1.0520 -5.2010 -9.8310 -8.1870 -0.3310 +9400.0 -0.0000 5.7424 10.6619 14.0220 15.2540 14.0320 10.3610 4.7090 -1.7250 -6.4410 -4.3860 4.2420 +9850.0 -0.0000 6.2382 11.6161 15.3630 16.8855 15.8390 12.2285 6.5375 0.0130 -4.7460 -2.4855 6.5285 diff --git a/specsim/data/sdss/throughput/eboss-fiberloss-elg.dat b/specsim/data/sdss/throughput/eboss-fiberloss-elg.dat new file mode 100644 index 0000000..69fcab1 --- /dev/null +++ b/specsim/data/sdss/throughput/eboss-fiberloss-elg.dat @@ -0,0 +1,14436 @@ +# Fiber input geometric acceptance +# 1.1 arcsec seeing with Moffat beta=3.5 scales as (6355/lambda)^0.2 +# Calculation removes 0.22 arcsec existing Mayall blur then adds DESI blur +# Lateral positioner offset=0.2 arcsec +# +# ELG with exponential half-light radius=0.45 arcsec +# +# Wavelength FiberAcceptance +col1 col2 +2980.0 0.38185 +2980.1 0.38185 +2980.2 0.38185 +2980.2999999999997 0.38185 +2980.3999999999996 0.38185 +2980.4999999999995 0.38185 +2980.5999999999995 0.38185 +2980.6999999999994 0.38185 +2980.7999999999993 0.38185 +2980.899999999999 0.38185 +2980.999999999999 0.38185 +2981.099999999999 0.38185 +2981.199999999999 0.38185 +2981.299999999999 0.38185 +2981.3999999999987 0.38185 +2981.4999999999986 0.38185 +2981.5999999999985 0.38185 +2981.6999999999985 0.38185 +2981.7999999999984 0.38185 +2981.8999999999983 0.38185 +2981.999999999998 0.38185 +2982.099999999998 0.38185 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