diff --git a/.travis.yml b/.travis.yml index 2a0adb49a..5375c262f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -43,7 +43,8 @@ env: # - SCIPY_VERSION=0.16 - ASTROPY_VERSION=1.2.1 # - SPHINX_VERSION=1.3 - - DESIUTIL_VERSION=1.8.0 + # - DESIUTIL_VERSION=1.8.0 + - DESIUTIL_VERSION=master - DESIMODEL_VERSION=0.5.0 - DESIMODEL_DATA=branches/test-0.5.0 - DESISIM_TESTDATA_VERSION=0.3.2 diff --git a/doc/changes.rst b/doc/changes.rst index 3efe7a74a..088288b83 100644 --- a/doc/changes.rst +++ b/doc/changes.rst @@ -9,6 +9,7 @@ desisim change log * pixsim add new required keywords DOSVER, FEEVER, DETECTOR * rewrote lya_spectra to achieve factor of 10 speedup. * COSMO (astropy.cosmology setup) is a new optional keyword for qso_desi_templates. +* minor enhancements to templates code 0.17.1 (2016-12-05) ------------------- diff --git a/doc/nb/.ipynb_checkpoints/mws-mock-checkpoint.ipynb b/doc/nb/.ipynb_checkpoints/mws-mock-checkpoint.ipynb deleted file mode 100644 index bf1c39aaf..000000000 --- a/doc/nb/.ipynb_checkpoints/mws-mock-checkpoint.ipynb +++ /dev/null @@ -1,367 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from astropy.table import Table, Column\n", - "from astropy.io import fits\n", - "\n", - "import seaborn as sns\n", - "from desisim.templates import STAR\n", - "\n", - "sns.set(style='white', font_scale=1.6, palette='deep')\n", - "%matplotlib inline\n", - "\n", - "LIGHT = 2.99792458E5 #- speed of light in km/s" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ColDefs(\n", - " name = 'X'; format = 'E'; unit = 'kpc'\n", - " name = 'Y'; format = 'E'; unit = 'kpc'\n", - " name = 'Z'; format = 'E'; unit = 'kpc'\n", - " name = 'l'; format = 'E'; unit = 'deg'\n", - " name = 'b'; format = 'E'; unit = 'deg'\n", - " name = 'RA'; format = 'D'; unit = 'deg'\n", - " name = 'DEC'; format = 'D'; unit = 'deg'\n", - " name = 'pm_l'; format = 'E'\n", - " name = 'pm_b'; format = 'E'\n", - " name = 'pm_RA'; format = 'E'\n", - " name = 'pm_DEC'; format = 'E'\n", - " name = 'pm_l_kms'; format = 'E'; unit = 'km s-1'\n", - " name = 'pm_b_kms'; format = 'E'; unit = 'km s-1'\n", - " name = 'pm_RA_kms'; format = 'E'; unit = 'km s-1'\n", - " name = 'pm_DEC_kms'; format = 'E'; unit = 'km s-1'\n", - " name = 'v_helio'; format = 'E'; unit = 'km s-1'\n", - " name = 'd_helio'; format = 'E'; unit = 'kpc'\n", - " name = 'DM'; format = 'E'; unit = 'mag'\n", - " name = 'ABV'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSu_true_nodust'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSu_true'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSu_obs'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSg_true_nodust'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSg_true'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSg_obs'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSr_true_nodust'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSr_true'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSr_obs'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSi_true_nodust'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSi_true'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSi_obs'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSz_true_nodust'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSz_true'; format = 'E'; unit = 'mag'\n", - " name = 'SDSSz_obs'; format = 'E'; unit = 'mag'\n", - " name = 'FeH'; format = 'E'\n", - " name = 'age'; format = 'E'\n", - " name = 'teff'; format = 'E'\n", - " name = 'logg'; format = 'E'\n", - " name = 'mtip'; format = 'E'\n", - " name = 'mact'; format = 'E'\n", - " name = 'smass'; format = 'E'\n", - " name = 'popid'; format = 'J'\n", - " name = 'brickname'; format = '8A'\n", - " name = 'brickid'; format = 'K'\n", - " name = 'vRcyl'; format = 'E'; unit = 'km s-1'\n", - " name = 'vPHIcyl'; format = 'E'; unit = 'km s-1'\n", - " name = 'vZcyl'; format = 'E'; unit = 'km s-1'\n", - " name = 'vX'; format = 'E'; unit = 'km s-1'\n", - " name = 'vY'; format = 'E'; unit = 'km s-1'\n", - " name = 'vZ'; format = 'E'; unit = 'km s-1'\n", - " name = 'vU'; format = 'E'; unit = 'km s-1'\n", - " name = 'vV'; format = 'E'; unit = 'km s-1'\n", - " name = 'vW'; format = 'E'; unit = 'km s-1'\n", - " name = 'objid'; format = 'K'\n", - ")\n" - ] - } - ], - "source": [ - "cat = fits.getdata('allsky_galaxia_desi_3250p100.fits', ext=1)[:1000]\n", - "print(cat.columns)\n", - "nstar = len(cat)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " REDSHIFT MAG TEFF LOGG FEH \n", - "------------ ------- ------- ------- -----------\n", - " 6.01848e-05 15.8917 3282.58 5.13229 -0.0315856\n", - " 7.99531e-06 15.8664 3312.49 5.14478 -0.00470402\n", - " 1.74716e-05 15.5964 3334.0 5.10969 0.125893\n", - " 5.70486e-05 15.3146 3410.2 5.09782 0.00320513\n", - " 9.38985e-05 15.8247 3458.58 5.05803 0.09498\n", - " 6.27923e-07 16.7426 3363.57 5.13803 -0.0633323\n", - " 6.7137e-05 15.4234 3541.03 5.01888 0.117763\n", - " 1.90811e-05 15.9689 3328.64 5.12838 0.0616664\n", - " -3.0621e-05 16.0427 3437.96 5.08739 0.0287824\n", - " 4.54792e-05 16.8698 3183.28 5.06934 0.0716745\n", - " ... ... ... ... ...\n", - "-2.19293e-05 15.5301 4670.36 4.63824 0.134668\n", - " 1.39156e-05 15.633 4796.29 4.66383 -0.198207\n", - " 0.000101043 16.0687 4532.31 4.69746 -0.137685\n", - " 7.15131e-05 16.0526 4577.26 4.69566 -0.176918\n", - " 4.90442e-05 15.5463 4729.08 4.61933 0.174833\n", - " 5.23339e-05 16.9829 4197.07 4.78854 -0.110437\n", - " 0.000110215 16.7676 4273.42 4.73282 0.130931\n", - " 0.000110136 16.7092 4342.24 4.74698 -0.126916\n", - " 3.05512e-05 16.383 4447.55 4.70958 -0.0844261\n", - " 4.06572e-05 16.2816 4470.27 4.69687 -0.0361054\n", - " 8.89215e-06 15.9 4604.76 4.63764 0.216518\n", - "Length = 1000 rows\n" - ] - } - ], - "source": [ - "# Build the input Table needed by desisim.templates.\n", - "star_properties = Table()\n", - "star_properties.add_column(Column(name='REDSHIFT', length=nstar, dtype='f4'))\n", - "star_properties.add_column(Column(name='MAG', length=nstar, dtype='f4'))\n", - "star_properties.add_column(Column(name='TEFF', length=nstar, dtype='f4'))\n", - "star_properties.add_column(Column(name='LOGG', length=nstar, dtype='f4'))\n", - "star_properties.add_column(Column(name='FEH', length=nstar, dtype='f4'))\n", - "\n", - "normfilter = 'sdss2010-r'\n", - "star_properties['REDSHIFT'] = cat['v_helio'] / LIGHT\n", - "star_properties['MAG'] = cat['SDSSr_true_nodust']\n", - "star_properties['TEFF'] = 10**cat['teff']\n", - "star_properties['LOGG'] = cat['logg']\n", - "star_properties['FEH'] = cat['FeH']\n", - "\n", - "print(star_properties)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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4ysE5nkukKrLU9P0G+AZwBfASMcu8iIhIMxq67ogxntekwu+//lwu5xGppixJ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\nSK0sQgwk6Eg6sdmCuFna3d0LXTR+Y2Z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IwLW6SDbCAMJt726FI51g9m/h1T/DH/4k6zgLdA2gRdixHvqOkhSah/TfzEIc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qqLIyO9dlzQ9eQMCbuQPo5vGfWmMiO8ckfcZIEGN97RW8hZK5l+i7X+PpMWiEJS06UlzuczJeiE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\nU8/rjMu+u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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", - "_, _, _ = ax1.hist(star_properties['MAG'])\n", - "ax1.set_ylabel('Number of Stars per bin')\n", - "ax1.set_xlabel('SDSS r (AB mag)')\n", - "\n", - "_, _, _ = ax2.hist(star_properties['REDSHIFT'] * LIGHT)\n", - "ax2.set_xlabel('Heliocentric Redshift')\n", - "\n", - "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(6, 8))\n", - "ax1.scatter(star_properties['TEFF'], star_properties['LOGG'])\n", - "ax1.set_ylabel('log $(g / cm s^{-2})$')\n", - "ax1.margins(0.05)\n", - "\n", - "ax2.scatter(star_properties['TEFF'], star_properties['FEH'])\n", - "ax2.set_xlabel('T$_{eff}$ (K)')\n", - "ax2.set_ylabel('[Fe/H]')\n", - "ax2.margins(0.05)\n", - "fig.subplots_adjust(hspace=0.04)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:io.py:611:read_basis_templates: Reading /Users/ioannis/research/projects/desi/spectro/templates/basis_templates/v2.2/star_templates_v2.1.fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:DESI:Reading /Users/ioannis/research/projects/desi/spectro/templates/basis_templates/v2.2/star_templates_v2.1.fits\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:templates.py:1296:make_star_templates: 35 spectra could not be computed given the input priors!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:DESI:35 spectra could not be computed given the input priors!\n" - ] - } - ], - "source": [ - "seed = 123\n", - "star = STAR(normfilter=normfilter)\n", - "flux, wave, meta = star.make_templates(seed=seed, star_properties=star_properties)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OBJTYPE TEMPLATEID SEED REDSHIFT ... AGE TEFF LOGG FEH \n", - " ... Gyr K m / s2 \n", - "------- ---------- ---------- ----------- ... ---- ------- ------- -----------\n", - " STAR 0 2991312382 6.01848e-05 ... -1.0 3282.58 5.13229 -0.0315856\n", - " STAR 1 3062119789 7.99531e-06 ... -1.0 3312.49 5.14478 -0.00470402\n", - " STAR 2 1228959102 1.74716e-05 ... -1.0 3334.0 5.10969 0.125893\n", - " STAR 3 1840268610 5.70486e-05 ... -1.0 3410.2 5.09782 0.00320513\n", - " STAR 4 974319580 9.38985e-05 ... -1.0 3458.58 5.05803 0.09498\n", - " STAR 5 2967327842 6.27923e-07 ... -1.0 3363.57 5.13803 -0.0633323\n", - " STAR 6 2367878886 6.7137e-05 ... -1.0 3541.03 5.01888 0.117763\n", - " STAR 7 3088727057 1.90811e-05 ... -1.0 3328.64 5.12838 0.0616664\n", - " STAR 8 3090095699 -3.0621e-05 ... -1.0 3437.96 5.08739 0.0287824\n", - " STAR 9 2109339754 4.54792e-05 ... -1.0 3183.28 5.06934 0.0716745\n", - " ... ... ... ... ... ... ... ... ...\n", - " STAR 990 4211633505 1.39156e-05 ... -1.0 4796.29 4.66383 -0.198207\n", - " STAR 991 1436232733 0.000101043 ... -1.0 4532.31 4.69746 -0.137685\n", - " STAR 992 3791223403 7.15131e-05 ... -1.0 4577.26 4.69566 -0.176918\n", - " STAR 993 1211957270 4.90442e-05 ... -1.0 4729.08 4.61933 0.174833\n", - " STAR 994 3949104175 5.23339e-05 ... -1.0 4197.07 4.78854 -0.110437\n", - " STAR 995 1670951350 0.000110215 ... -1.0 4273.42 4.73282 0.130931\n", - " STAR 996 1784574163 0.000110136 ... -1.0 4342.24 4.74698 -0.126916\n", - " STAR 997 2445381101 3.05512e-05 ... -1.0 4447.55 4.70958 -0.0844261\n", - " STAR 998 3198099070 4.06572e-05 ... -1.0 4470.27 4.69687 -0.0361054\n", - " STAR 999 2423849465 8.89215e-06 ... -1.0 4604.76 4.63764 0.216518\n", - "Length = 1000 rows\n" - ] - } - ], - "source": [ - "print(meta)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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beSivtYBjfZioIjBA/QnBQvEUiFK5MjduP/P3URg8m7JZoyMZ3aDbDhsfwy2V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\nyh8DlheRWod8V6pTf8Bf6wcWBMZHYXB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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(wave, flux[0, :])\n", - "plt.xlabel('Observed Wavelength ($\\AA$)')\n", - "plt.ylabel('F$_{\\lambda}$ (erg / s / cm$^{2}$ / $\\AA$)')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/py/desisim/io.py b/py/desisim/io.py index 0f1cbd2ff..60d91ff3b 100644 --- a/py/desisim/io.py +++ b/py/desisim/io.py @@ -559,7 +559,7 @@ def _qso_format_version(filename): else: raise IOError('Unknown QSO basis template format '+filename) -def read_basis_templates(objtype, outwave=None, nspec=None, infile=None): +def read_basis_templates(objtype, outwave=None, nspec=None, infile=None, onlymeta=False): """Return the basis (continuum) templates for a given object type. Optionally returns a randomly selected subset of nspec spectra sampled at wavelengths outwave. @@ -572,6 +572,7 @@ def read_basis_templates(objtype, outwave=None, nspec=None, infile=None): infile (str, optional): full path to input template file to read, over-riding the contents of the $DESI_BASIS_TEMPLATES environment variable. + onlymeta (Bool, optional): read just the metadata table and return Returns: Tuple of (outflux, outwave, meta) where @@ -598,6 +599,10 @@ def read_basis_templates(objtype, outwave=None, nspec=None, infile=None): if infile is None: infile = find_basis_template(ltype) + if onlymeta: + log.info('Reading {} metadata.'.format(infile)) + return Table(fits.getdata(infile, 1)) + log.info('Reading {}'.format(infile)) if objtype.upper() == 'QSO': @@ -769,3 +774,18 @@ def empty_metatable(nmodel=1, objtype='ELG', add_SNeIa=None): meta['OBJTYPE'] = objtype.upper() return meta + +def empty_star_properties(nstar=1): + """Initialize a "star_properties" table for desisim.templates.""" + from astropy.table import Table, Column + + star_properties = Table() + star_properties.add_column(Column(name='REDSHIFT', length=nstar, dtype='f4')) + star_properties.add_column(Column(name='MAG', length=nstar, dtype='f4')) + star_properties.add_column(Column(name='TEFF', length=nstar, dtype='f4')) + star_properties.add_column(Column(name='LOGG', length=nstar, dtype='f4')) + star_properties.add_column(Column(name='FEH', length=nstar, dtype='f4')) + star_properties.add_column(Column(name='SEED', length=nstar, dtype='int64', + data=np.zeros(nstar)-1)) + + return star_properties diff --git a/py/desisim/targets.py b/py/desisim/targets.py index bab615440..fc65a649f 100644 --- a/py/desisim/targets.py +++ b/py/desisim/targets.py @@ -14,7 +14,7 @@ from astropy.table import Table, Column, hstack from desimodel.focalplane import FocalPlane -from desisim.io import empty_metatable +from desisim.io import empty_metatable, empty_star_properties import desimodel.io from desispec.log import get_logger log = get_logger() @@ -483,3 +483,15 @@ def sample_nz(objtype, n): #- Sample that distribution x = np.random.uniform(0.0, 1.0, size=n) return np.interp(x, cdf, zhi) + + +def _default_wave(wavemin=None, wavemax=None, dw=0.2): + '''Construct and return the default wavelength vector.''' + + if wavemin is None: + wavemin = desimodel.io.load_throughput('b').wavemin + if wavemax is None: + wavemax = desimodel.io.load_throughput('z').wavemax + wave = np.arange(round(wavemin, 1), wavemax, dw) + + return wave diff --git a/py/desisim/templates.py b/py/desisim/templates.py index d9a95f113..7a745d341 100755 --- a/py/desisim/templates.py +++ b/py/desisim/templates.py @@ -17,65 +17,6 @@ LIGHT = 2.99792458E5 #- speed of light in km/s -class GaussianMixtureModel(object): - """Read and sample from a pre-defined Gaussian mixture model. - - """ - def __init__(self, weights, means, covars, covtype): - self.weights = weights - self.means = means - self.covars = covars - self.covtype = covtype - self.n_components, self.n_dimensions = self.means.shape - - @staticmethod - def save(model, filename): - from astropy.io import fits - hdus = fits.HDUList() - hdr = fits.Header() - hdr['covtype'] = model.covariance_type - hdus.append(fits.ImageHDU(model.weights_, name='weights', header=hdr)) - hdus.append(fits.ImageHDU(model.means_, name='means')) - hdus.append(fits.ImageHDU(model.covars_, name='covars')) - hdus.writeto(filename, clobber=True) - - @staticmethod - def load(filename): - from astropy.io import fits - hdus = fits.open(filename, memmap=False) - hdr = hdus[0].header - covtype = hdr['covtype'] - model = GaussianMixtureModel( - hdus['weights'].data, hdus['means'].data, hdus['covars'].data, covtype) - hdus.close() - return model - - def sample(self, n_samples=1, random_state=None): - - if self.covtype != 'full': - return NotImplementedError( - 'covariance type "{0}" not implemented yet.'.format(self.covtype)) - - # Code adapted from sklearn's GMM.sample() - if random_state is None: - random_state = np.random.RandomState() - - weight_cdf = np.cumsum(self.weights) - X = np.empty((n_samples, self.n_dimensions)) - rand = random_state.rand(n_samples) - # decide which component to use for each sample - comps = weight_cdf.searchsorted(rand) - # for each component, generate all needed samples - for comp in range(self.n_components): - # occurrences of current component in X - comp_in_X = (comp == comps) - # number of those occurrences - num_comp_in_X = comp_in_X.sum() - if num_comp_in_X > 0: - X[comp_in_X] = random_state.multivariate_normal( - self.means[comp], self.covars[comp], num_comp_in_X) - return X - class EMSpectrum(object): """Construct a complete nebular emission-line spectrum. @@ -117,6 +58,7 @@ class EMSpectrum(object): def __init__(self, minwave=3650.0, maxwave=7075.0, cdelt_kms=20.0, log10wave=None): from pkg_resources import resource_filename from astropy.table import Table, Column, vstack + from desiutil.sklearn import GaussianMixtureModel # Build a wavelength array if one is not given. if log10wave is None: @@ -445,7 +387,7 @@ def make_galaxy_templates(self, nmodel=100, zrange=(0.6, 1.6), magrange=(21.0, 2 minlineflux=0.0, sne_rfluxratiorange=(0.01, 0.1), seed=None, redshift=None, mag=None, vdisp=None, input_meta=None, nocolorcuts=False, nocontinuum=False, - agnlike=False, restframe=False): + agnlike=False, novdisp=False, restframe=False): """Build Monte Carlo galaxy spectra/templates. This function chooses random subsets of the basis continuum spectra (for @@ -516,6 +458,8 @@ def make_galaxy_templates(self, nmodel=100, zrange=(0.6, 1.6), magrange=(21.0, 2 the output spectrum (useful for testing; default False). Note that this option automatically sets nocolorcuts to True and add_SNeIa to False. + novdisp (bool, optional): Do not velocity-blur the spectrum (default + False). agnlike (bool, optional): Adopt AGN-like emission-line ratios (e.g., for the LRGs and some BGS galaxies) (default False, meaning we adopt star-formation-like line-ratios). Option not yet supported. @@ -553,7 +497,7 @@ def make_galaxy_templates(self, nmodel=100, zrange=(0.6, 1.6), magrange=(21.0, 2 vzero = np.where(vdisp <= 0)[0] if len(vzero) > 0: - log.fatal('Velocity disperion is zero or negative in {} spectra!').format(len(vzero)) + log.fatal('Velocity dispersion is zero or negative!') raise ValueError if self.add_SNeIa: @@ -745,7 +689,7 @@ def make_galaxy_templates(self, nmodel=100, zrange=(0.6, 1.6), magrange=(21.0, 2 tempid = templateid[this] thisemflux = emflux * normlineflux[templateid[this]] - if nocontinuum: + if nocontinuum or novdisp: blurflux = restflux[this, :] * magnorm[this] else: blurflux = (self.vdispblur((restflux[this, :] - thisemflux), vdisp[ii]) + \ @@ -819,7 +763,7 @@ def make_templates(self, nmodel=100, zrange=(0.6, 1.6), rmagrange=(21.0, 23.4), oiiihbrange=(-0.5, 0.2), logvdisp_meansig=(1.9, 0.15), minoiiflux=0.0, sne_rfluxratiorange=(0.1, 1.0), redshift=None, mag=None, vdisp=None, seed=None, input_meta=None, nocolorcuts=False, - nocontinuum=False, agnlike=False, restframe=False): + nocontinuum=False, agnlike=False, novdisp=False, restframe=False): """Build Monte Carlo ELG spectra/templates. See the GALAXY.make_galaxy_templates function for documentation on the @@ -853,7 +797,8 @@ def make_templates(self, nmodel=100, zrange=(0.6, 1.6), rmagrange=(21.0, 23.4), minlineflux=minoiiflux, redshift=redshift, vdisp=vdisp, mag=mag, sne_rfluxratiorange=sne_rfluxratiorange, seed=seed, input_meta=input_meta, nocolorcuts=nocolorcuts, - nocontinuum=nocontinuum, agnlike=agnlike, restframe=restframe) + nocontinuum=nocontinuum, agnlike=agnlike, novdisp=novdisp, + restframe=restframe) return outflux, wave, meta @@ -898,7 +843,7 @@ def make_templates(self, nmodel=100, zrange=(0.01, 0.4), rmagrange=(15.0, 19.5), oiiihbrange=(-1.3, 0.6), logvdisp_meansig=(2.0, 0.17), minhbetaflux=0.0, sne_rfluxratiorange=(0.1, 1.0), redshift=None, mag=None, vdisp=None, seed=None, input_meta=None, nocolorcuts=False, - nocontinuum=False, agnlike=False, restframe=False): + nocontinuum=False, agnlike=False, novdisp=False, restframe=False): """Build Monte Carlo BGS spectra/templates. See the GALAXY.make_galaxy_templates function for documentation on the @@ -932,8 +877,9 @@ def make_templates(self, nmodel=100, zrange=(0.01, 0.4), rmagrange=(15.0, 19.5), minlineflux=minhbetaflux, redshift=redshift, vdisp=vdisp, mag=mag, sne_rfluxratiorange=sne_rfluxratiorange, seed=seed, input_meta=input_meta, nocolorcuts=nocolorcuts, - nocontinuum=nocontinuum, agnlike=agnlike, restframe=restframe) - + nocontinuum=nocontinuum, agnlike=agnlike, novdisp=novdisp, + restframe=restframe) + return outflux, wave, meta class LRG(GALAXY): @@ -968,7 +914,7 @@ def __init__(self, minwave=3600.0, maxwave=10000.0, cdelt=0.2, wave=None, def make_templates(self, nmodel=100, zrange=(0.5, 1.0), zmagrange=(19.0, 20.5), logvdisp_meansig=(2.3, 0.1), sne_rfluxratiorange=(0.1, 1.0), redshift=None, mag=None, vdisp=None, seed=None, - input_meta=None, nocolorcuts=False, agnlike=False, + input_meta=None, nocolorcuts=False, novdisp=False, agnlike=False, restframe=False): """Build Monte Carlo BGS spectra/templates. @@ -999,7 +945,7 @@ def make_templates(self, nmodel=100, zrange=(0.5, 1.0), zmagrange=(19.0, 20.5), logvdisp_meansig=logvdisp_meansig, redshift=redshift, vdisp=vdisp, mag=mag, sne_rfluxratiorange=sne_rfluxratiorange, seed=seed, input_meta=input_meta, nocolorcuts=nocolorcuts, - agnlike=agnlike, restframe=restframe) + novdisp=novdisp, agnlike=agnlike, restframe=restframe) # Pack into the metadata table some additional information. good = np.where(meta['TEMPLATEID'] != -1)[0] diff --git a/py/desisim/test/test_templates.py b/py/desisim/test/test_templates.py index 9e33f2931..aa6938c6f 100644 --- a/py/desisim/test/test_templates.py +++ b/py/desisim/test/test_templates.py @@ -168,10 +168,11 @@ def test_star_properties(self): for T in [STAR]: Tx = T(wave=self.wave) flux, wave, meta = Tx.make_templates(star_properties=star_properties, seed=self.seed) + #import pdb ; pdb.set_trace() badkeys = list() for key in meta.colnames: if key in star_properties.colnames: - if not np.all(meta[key] == star_properties[key]): + if not np.allclose(meta[key], star_properties[key]): badkeys.append(key) self.assertEqual(len(badkeys), 0, 'mismatch for spectral type {} in keys {}'.format(meta['OBJTYPE'][0], badkeys))