__init__.py 35.8 KB
Newer Older
Yannick Roehlly's avatar
Yannick Roehlly committed
1
# -*- coding: utf-8 -*-
2 3
# Copyright (C) 2012, 2013 Centre de données Astrophysiques de Marseille
# Licensed under the CeCILL-v2 licence - see Licence_CeCILL_V2-en.txt
Yannick Roehlly's avatar
Yannick Roehlly committed
4
# Authors: Yannick Roehlly, Médéric Boquien, Laure Ciesla
Yannick Roehlly's avatar
Yannick Roehlly committed
5

6
"""
Yannick Roehlly's avatar
Yannick Roehlly committed
7 8 9 10 11 12 13 14 15 16
This script is used the build pcigale internal database containing:
- The various filter transmission tables;
- The Maraston 2005 single stellar population (SSP) data;
- The Dale and Helou 2002 infra-red templates.

"""
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
import glob
17
import io
18
import itertools
Yannick Roehlly's avatar
Yannick Roehlly committed
19 20
import numpy as np
from scipy import interpolate
21
import scipy.constants as cst
22
from astropy.table import Table
23
from pcigale.data import (Database, Filter, M2005, BC03, Fritz2006,
24
                          Dale2014, DL2007, DL2014, NebularLines,
25
                          NebularContinuum, Schreiber2016, THEMIS)
Yannick Roehlly's avatar
Yannick Roehlly committed
26 27


28 29 30 31 32
def read_bc03_ssp(filename):
    """Read a Bruzual and Charlot 2003 ASCII SSP file

    The ASCII SSP files of Bruzual and Charlot 2003 have se special structure.
    A vector is stored with the number of values followed by the values
Yannick Roehlly's avatar
Yannick Roehlly committed
33
    separated by a space (or a carriage return). There are the time vector, 5
34 35 36 37 38 39 40 41 42 43 44 45
    (for Chabrier IMF) or 6 lines (for Salpeter IMF) that we don't care of,
    then the wavelength vector, then the luminosity vectors, each followed by
    a 52 value table, then a bunch of other table of information that are also
    in the *colors files.

    Parameters
    ----------
    filename : string

    Returns
    -------
    time_grid: numpy 1D array of floats
Yannick Roehlly's avatar
Yannick Roehlly committed
46
              Vector of the time grid of the SSP in Myr.
47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73
    wavelength: numpy 1D array of floats
                Vector of the wavelength grid of the SSP in nm.
    spectra: numpy 2D array of floats
             Array containing the SSP spectra, first axis is the wavelength,
             second one is the time.

    """

    def file_structure_generator():
        """Generator used to identify table lines in the SSP file

        In the SSP file, the vectors are store one next to the other, but
        there are 5 informational lines after the time vector. We use this
        generator to the if we are on lines to read or not.
        """
        if "chab" in filename:
            bad_line_number = 5
        else:
            bad_line_number = 6
        yield("data")
        for i in range(bad_line_number):
            yield("bad")
        while True:
            yield("data")

    file_structure = file_structure_generator()
    # Are we in a data line or a bad one.
74
    what_line = next(file_structure)
75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116
    # Variable conting, in reverse order, the number of value still to
    # read for the read vector.
    counter = 0

    time_grid = []
    full_table = []
    tmp_table = []

    with open(filename) as file_:
        # We read the file line by line.
        for line in file_:
            if what_line == "data":
                # If we are in a "data" line, we analyse each number.
                for item in line.split():
                    if counter == 0:
                        # If counter is 0, then we are not reading a vector
                        # and the first number is the length of the next
                        # vector.
                        counter = int(item)
                    else:
                        # If counter > 0, we are currently reading a vector.
                        tmp_table.append(float(item))
                        counter -= 1
                        if counter == 0:
                            # We reached the end of the vector. If we have not
                            # yet store the time grid (the first table) we are
                            # currently reading it.
                            if time_grid == []:
                                time_grid = tmp_table[:]
                            # Else, we store the vector in the full table,
                            # only if its length is superior to 250 to get rid
                            # of the 52 item unknown vector and the 221 (time
                            # grid length) item vectors at the end of the
                            # file.
                            elif len(tmp_table) > 250:
                                full_table.append(tmp_table[:])

                            tmp_table = []

            # If at the end of a line, we have finished reading a vector, it's
            # time to change to the next structure context.
            if counter == 0:
117
                what_line = next(file_structure)
118

Yannick Roehlly's avatar
Yannick Roehlly committed
119
    # The time grid is in year, we want Myr.
120
    time_grid = np.array(time_grid, dtype=float)
121
    time_grid *= 1.e-6
122 123 124 125

    # The first "long" vector encountered is the wavelength grid. The value
    # are in Ångström, we convert it to nano-meter.
    wavelength = np.array(full_table.pop(0), dtype=float)
126
    wavelength *= 0.1
127 128 129 130

    # The luminosities are in Solar luminosity (3.826.10^33 ergs.s-1) per
    # Ångström, we convert it to W/nm.
    luminosity = np.array(full_table, dtype=float)
131
    luminosity *= 3.826e27
132 133 134 135 136 137 138 139
    # Transposition to have the time in the second axis.
    luminosity = luminosity.transpose()

    # In the SSP, the time grid begins at 0, but not in the *colors file, so
    # we remove t=0 from the SSP.
    return time_grid[1:], wavelength, luminosity[:, 1:]


140
def build_filters(base):
141
    filters = []
142
    filters_dir = os.path.join(os.path.dirname(__file__), 'filters/')
Yannick Roehlly's avatar
Yannick Roehlly committed
143 144 145 146 147 148 149 150 151
    for filter_file in glob.glob(filters_dir + '*.dat'):
        with open(filter_file, 'r') as filter_file_read:
            filter_name = filter_file_read.readline().strip('# \n\t')
            filter_type = filter_file_read.readline().strip('# \n\t')
            filter_description = filter_file_read.readline().strip('# \n\t')
        filter_table = np.genfromtxt(filter_file)
        # The table is transposed to have table[0] containing the wavelength
        # and table[1] containing the transmission.
        filter_table = filter_table.transpose()
152

Yannick Roehlly's avatar
Yannick Roehlly committed
153 154 155
        # We convert the wavelength from Å to nm.
        filter_table[0] *= 0.1

156 157 158 159 160 161 162
        # We convert to energy if needed
        if filter_type == 'photon':
            filter_table[1] *= filter_table[0]
        elif filter_type != 'energy':
            raise ValueError("Filter transmission type can only be "
                             "'energy' or 'photon'.")

Yannick Roehlly's avatar
Yannick Roehlly committed
163 164 165
        print("Importing %s... (%s points)" % (filter_name,
                                               filter_table.shape[1]))

166
        new_filter = Filter(filter_name, filter_description, filter_table)
Yannick Roehlly's avatar
Yannick Roehlly committed
167

168 169 170
        # We normalise the filter and compute the pivot wavelength. If the
        # filter is a pseudo-filter used to compute line fluxes, it should not
        # be normalised.
171 172 173
        if not filter_name.startswith('PSEUDO'):
            new_filter.normalise()
        else:
174
            new_filter.pivot_wavelength = np.mean(
175 176
                filter_table[0][filter_table[1] > 0]
            )
177
        filters.append(new_filter)
Yannick Roehlly's avatar
Yannick Roehlly committed
178

179
    base.add_filters(filters)
Yannick Roehlly's avatar
Yannick Roehlly committed
180

Médéric Boquien's avatar
Médéric Boquien committed
181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
def build_filters_gazpar(base):
    filters = []
    filters_dir = os.path.join(os.path.dirname(__file__), 'filters_gazpar/')
    for filter_file in glob.glob(filters_dir + '**/*.pb', recursive=True):
        with open(filter_file, 'r') as filter_file_read:
            _ = filter_file_read.readline() # We use the filename for the name
            filter_type = filter_file_read.readline().strip('# \n\t')
            _ = filter_file_read.readline() # We do not yet use the calib type
            filter_desc = filter_file_read.readline().strip('# \n\t')

        filter_name = filter_file.replace(filters_dir, '')[:-3]
        filter_name = filter_name.replace('/', '.')

        filter_table = np.genfromtxt(filter_file)
        # The table is transposed to have table[0] containing the wavelength
        # and table[1] containing the transmission.
        filter_table = filter_table.transpose()
198

Médéric Boquien's avatar
Médéric Boquien committed
199 200 201
        # We convert the wavelength from Å to nm.
        filter_table[0] *= 0.1

202 203 204 205 206 207 208
        # We convert to energy if needed
        if filter_type == 'photon':
            filter_table[1] *= filter_table[0]
        elif filter_type != 'energy':
            raise ValueError("Filter transmission type can only be "
                             "'energy' or 'photon'.")

Médéric Boquien's avatar
Médéric Boquien committed
209 210 211
        print("Importing %s... (%s points)" % (filter_name,
                                               filter_table.shape[1]))

212
        new_filter = Filter(filter_name, filter_desc, filter_table)
Médéric Boquien's avatar
Médéric Boquien committed
213

214 215 216
        # We normalise the filter and compute the pivot wavelength. If the
        # filter is a pseudo-filter used to compute line fluxes, it should not
        # be normalised.
Médéric Boquien's avatar
Médéric Boquien committed
217 218 219
        if not filter_name.startswith('PSEUDO'):
            new_filter.normalise()
        else:
220
            new_filter.pivot_wavelength = np.mean(
Médéric Boquien's avatar
Médéric Boquien committed
221 222 223 224 225
                filter_table[0][filter_table[1] > 0]
            )
        filters.append(new_filter)

    base.add_filters(filters)
226 227 228

def build_m2005(base):
    m2005_dir = os.path.join(os.path.dirname(__file__), 'maraston2005/')
Yannick Roehlly's avatar
Yannick Roehlly committed
229

Yannick Roehlly's avatar
Yannick Roehlly committed
230
    # Age grid (1 Myr to 13.7 Gyr with 1 Myr step)
231 232
    time_grid = np.arange(1, 13701)
    fine_time_grid = np.linspace(0.1, 13700, 137000)
Yannick Roehlly's avatar
Yannick Roehlly committed
233 234 235 236 237 238 239 240 241 242 243 244 245 246 247

    # Transpose the table to have access to each value vector on the first
    # axis
    kroupa_mass = np.genfromtxt(m2005_dir + 'stellarmass.kroupa').transpose()
    salpeter_mass = \
        np.genfromtxt(m2005_dir + '/stellarmass.salpeter').transpose()

    for spec_file in glob.glob(m2005_dir + '*.rhb'):

        print("Importing %s..." % spec_file)

        spec_table = np.genfromtxt(spec_file).transpose()
        metallicity = spec_table[1, 0]

        if 'krz' in spec_file:
248
            imf = 'krou'
Yannick Roehlly's avatar
Yannick Roehlly committed
249 250
            mass_table = np.copy(kroupa_mass)
        elif 'ssz' in spec_file:
251
            imf = 'salp'
Yannick Roehlly's avatar
Yannick Roehlly committed
252 253 254 255 256
            mass_table = np.copy(salpeter_mass)
        else:
            raise ValueError('Unknown IMF!!!')

        # Keep only the actual metallicity values in the mass table
257 258 259 260
        # we don't take the first column which contains metallicity.
        # We also eliminate the turn-off mas which makes no send for composite
        # populations.
        mass_table = mass_table[1:7, mass_table[0] == metallicity]
Yannick Roehlly's avatar
Yannick Roehlly committed
261

262 263 264 265 266 267 268 269
        # Regrid the SSP data to the evenly spaced time grid. In doing so we
        # assume 10 bursts every 0.1 Myr over a period of 1 Myr in order to
        # capture short evolutionary phases.
        # The time grid starts after 0.1 Myr, so we assume the value is the same
        # as the first actual time step.
        mass_table = interpolate.interp1d(mass_table[0] * 1e3, mass_table[1:],
                                          assume_sorted=True)(fine_time_grid)
        mass_table = np.mean(mass_table.reshape(5, -1, 10), axis=-1)
Yannick Roehlly's avatar
Yannick Roehlly committed
270

271 272 273
        # Extract the age and convert from Gyr to Myr
        ssp_time = np.unique(spec_table[0]) * 1e3
        spec_table = spec_table[1:]
Yannick Roehlly's avatar
Yannick Roehlly committed
274 275

        # Remove the metallicity column from the spec table
276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296
        spec_table = spec_table[1:]

        # Extract the wavelength and convert from Å to nm
        ssp_wave = spec_table[0][:1221] * 0.1
        spec_table = spec_table[1:]

        # Extra the fluxes and convert from erg/s/Å to W/nm
        ssp_lumin = spec_table[0].reshape(ssp_time.size, ssp_wave.size).T
        ssp_lumin *= 10 * 1e-7

        # We have to do the interpolation-averaging in several blocks as it is
        # a bit RAM intensive
        ssp_lumin_interp = np.empty((ssp_wave.size, time_grid.size))
        for i in range(0, ssp_wave.size, 100):
            fill_value = (ssp_lumin[i:i+100, 0], ssp_lumin[i:i+100, -1])
            ssp_interp = interpolate.interp1d(ssp_time, ssp_lumin[i:i+100, :],
                                              fill_value=fill_value,
                                              bounds_error=False,
                                              assume_sorted=True)(fine_time_grid)
            ssp_interp = ssp_interp.reshape(ssp_interp.shape[0], -1, 10)
            ssp_lumin_interp[i:i+100, :] = np.mean(ssp_interp, axis=-1)
297

298 299 300
        # To avoid the creation of waves when interpolating, we refine the grid
        # beyond 10 μm following a log scale in wavelength. The interpolation
        # is also done in log space as the spectrum is power-law-like
301
        ssp_wave_resamp = np.around(np.logspace(np.log10(10000),
302
                                                   np.log10(160000), 50))
303 304 305 306
        argmin = np.argmin(10000.-ssp_wave > 0)-1
        ssp_lumin_resamp = 10.**interpolate.interp1d(
                                    np.log10(ssp_wave[argmin:]),
                                    np.log10(ssp_lumin_interp[argmin:, :]),
307
                                    assume_sorted=True,
308
                                    axis=0)(np.log10(ssp_wave_resamp))
309

310 311 312
        ssp_wave = np.hstack([ssp_wave[:argmin+1], ssp_wave_resamp])
        ssp_lumin = np.vstack([ssp_lumin_interp[:argmin+1, :],
                               ssp_lumin_resamp])
313

314 315 316 317 318
        # Use Z value for metallicity, not log([Z/H])
        metallicity = {-1.35: 0.001,
                       -0.33: 0.01,
                       0.0: 0.02,
                       0.35: 0.04}[metallicity]
Yannick Roehlly's avatar
Yannick Roehlly committed
319

320 321
        base.add_m2005(M2005(imf, metallicity, time_grid, ssp_wave,
                             mass_table, ssp_lumin))
Yannick Roehlly's avatar
Yannick Roehlly committed
322 323


324 325
def build_bc2003(base, res):
    bc03_dir = os.path.join(os.path.dirname(__file__), 'bc03/')
326

327 328
    # Time grid (1 Myr to 14 Gyr with 1 Myr step)
    time_grid = np.arange(1, 14000)
329
    fine_time_grid = np.linspace(0.1, 13999, 139990)
330 331 332 333 334 335 336 337 338 339 340 341

    # Metallicities associated to each key
    metallicity = {
        "m22": 0.0001,
        "m32": 0.0004,
        "m42": 0.004,
        "m52": 0.008,
        "m62": 0.02,
        "m72": 0.05
    }

    for key, imf in itertools.product(metallicity, ["salp", "chab"]):
342 343 344 345 346 347
        ssp_filename = "{}bc2003_{}_{}_{}_ssp.ised_ASCII".format(bc03_dir, res,
                                                                 key, imf)
        color3_filename = "{}bc2003_lr_{}_{}_ssp.3color".format(bc03_dir, key,
                                                                imf)
        color4_filename = "{}bc2003_lr_{}_{}_ssp.4color".format(bc03_dir, key,
                                                                imf)
348

349
        print("Importing {}...".format(ssp_filename))
350 351 352 353 354

        # Read the desired information from the color files
        color_table = []
        color3_table = np.genfromtxt(color3_filename).transpose()
        color4_table = np.genfromtxt(color4_filename).transpose()
355 356 357
        color_table.append(color4_table[6])        # Mstar
        color_table.append(color4_table[7])        # Mgas
        color_table.append(10 ** color3_table[5])  # NLy
358 359 360 361 362

        color_table = np.array(color_table)

        ssp_time, ssp_wave, ssp_lumin = read_bc03_ssp(ssp_filename)

363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385
        # Regrid the SSP data to the evenly spaced time grid. In doing so we
        # assume 10 bursts every 0.1 Myr over a period of 1 Myr in order to
        # capture short evolutionary phases.
        # The time grid starts after 0.1 Myr, so we assume the value is the same
        # as the first actual time step.
        fill_value = (color_table[:, 0], color_table[:, -1])
        color_table = interpolate.interp1d(ssp_time, color_table,
                                           fill_value=fill_value,
                                           bounds_error=False,
                                           assume_sorted=True)(fine_time_grid)
        color_table = np.mean(color_table.reshape(3, -1, 10), axis=-1)

        # We have to do the interpolation-averaging in several blocks as it is
        # a bit RAM intensive
        ssp_lumin_interp = np.empty((ssp_wave.size, time_grid.size))
        for i in range(0, ssp_wave.size, 100):
            fill_value = (ssp_lumin[i:i+100, 0], ssp_lumin[i:i+100, -1])
            ssp_interp = interpolate.interp1d(ssp_time, ssp_lumin[i:i+100, :],
                                              fill_value=fill_value,
                                              bounds_error=False,
                                              assume_sorted=True)(fine_time_grid)
            ssp_interp = ssp_interp.reshape(ssp_interp.shape[0], -1, 10)
            ssp_lumin_interp[i:i+100, :] = np.mean(ssp_interp, axis=-1)
386

387 388 389 390 391 392 393 394
        # To avoid the creation of waves when interpolating, we refine the grid
        # beyond 10 μm following a log scale in wavelength. The interpolation
        # is also done in log space as the spectrum is power-law-like
        ssp_wave_resamp = np.around(np.logspace(np.log10(10000),
                                                np.log10(160000), 50))
        argmin = np.argmin(10000.-ssp_wave > 0)-1
        ssp_lumin_resamp = 10.**interpolate.interp1d(
                                    np.log10(ssp_wave[argmin:]),
395
                                    np.log10(ssp_lumin_interp[argmin:, :]),
396 397 398 399
                                    assume_sorted=True,
                                    axis=0)(np.log10(ssp_wave_resamp))

        ssp_wave = np.hstack([ssp_wave[:argmin+1], ssp_wave_resamp])
400 401
        ssp_lumin = np.vstack([ssp_lumin_interp[:argmin+1, :],
                               ssp_lumin_resamp])
402

403
        base.add_bc03(BC03(
404 405 406 407 408 409 410 411
            imf,
            metallicity[key],
            time_grid,
            ssp_wave,
            color_table,
            ssp_lumin
        ))

412

413
def build_dale2014(base):
414
    models = []
415 416 417
    dale2014_dir = os.path.join(os.path.dirname(__file__), 'dale2014/')

    # Getting the alpha grid for the templates
418
    d14cal = np.genfromtxt(dale2014_dir + 'dhcal.dat')
419 420 421
    alpha_grid = d14cal[:, 1]

    # Getting the lambda grid for the templates and convert from microns to nm.
422
    first_template = np.genfromtxt(dale2014_dir + 'spectra.0.00AGN.dat')
423 424
    wave = first_template[:, 0] * 1E3

Médéric Boquien's avatar
Médéric Boquien committed
425 426 427 428
    # Getting the stellar emission and interpolate it at the same wavelength
    # grid
    stell_emission_file = np.genfromtxt(dale2014_dir +
                                        'stellar_SED_age13Gyr_tau10Gyr.spec')
429
    # A -> to nm
Médéric Boquien's avatar
Médéric Boquien committed
430
    wave_stell = stell_emission_file[:, 0] * 0.1
431
    # W/A -> W/nm
Médéric Boquien's avatar
Médéric Boquien committed
432 433
    stell_emission = stell_emission_file[:, 1] * 10
    stell_emission_interp = np.interp(wave, wave_stell, stell_emission)
434 435 436 437 438 439 440 441 442 443 444 445

    # The models are in nuFnu and contain stellar emission.
    # We convert this to W/nm and remove the stellar emission.

    # Emission from dust heated by SB
    fraction = 0.0
    filename = dale2014_dir + "spectra.0.00AGN.dat"
    print("Importing {}...".format(filename))
    datafile = open(filename)
    data = "".join(datafile.readlines())
    datafile.close()

446
    for al in range(1, len(alpha_grid)+1, 1):
Médéric Boquien's avatar
Médéric Boquien committed
447 448 449
        lumin_with_stell = np.genfromtxt(io.BytesIO(data.encode()),
                                         usecols=(al))
        lumin_with_stell = pow(10, lumin_with_stell) / wave
450 451
        constant = lumin_with_stell[7] / stell_emission_interp[7]
        lumin = lumin_with_stell - stell_emission_interp * constant
Médéric Boquien's avatar
Médéric Boquien committed
452 453 454
        lumin[lumin < 0] = 0
        lumin[wave < 2E3] = 0
        norm = np.trapz(lumin, x=wave)
455
        lumin /= norm
456

457
        models.append(Dale2014(fraction, alpha_grid[al-1], wave, lumin))
458
    # Emission from dust heated by AGN - Quasar template
459
    filename = dale2014_dir + "shi_agn.regridded.extended.dat"
460 461
    print("Importing {}...".format(filename))

462 463 464 465
    wave, lumin_quasar = np.genfromtxt(filename, unpack=True)
    wave *= 1e3
    lumin_quasar = 10**lumin_quasar / wave
    norm = np.trapz(lumin_quasar, x=wave)
466
    lumin_quasar /= norm
467

468 469 470
    models.append(Dale2014(1.0, 0.0, wave, lumin_quasar))

    base.add_dale2014(models)
471

472

473
def build_dl2007(base):
474
    models = []
475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492
    dl2007_dir = os.path.join(os.path.dirname(__file__), 'dl2007/')

    qpah = {
        "00": 0.47,
        "10": 1.12,
        "20": 1.77,
        "30": 2.50,
        "40": 3.19,
        "50": 3.90,
        "60": 4.58
    }

    umaximum = ["1e3", "1e4", "1e5", "1e6"]
    uminimum = ["0.10", "0.15", "0.20", "0.30", "0.40", "0.50", "0.70",
                "0.80", "1.00", "1.20", "1.50", "2.00", "2.50", "3.00",
                "4.00", "5.00", "7.00", "8.00", "10.0", "12.0", "15.0",
                "20.0", "25.0"]

493
    # Mdust/MH used to retrieve the dust mass as models as given per atom of H
Médéric Boquien's avatar
Médéric Boquien committed
494 495
    MdMH = {"00": 0.0100, "10": 0.0100, "20": 0.0101, "30": 0.0102,
            "40": 0.0102, "50": 0.0103, "60": 0.0104}
496

497 498 499 500 501 502 503 504 505 506 507 508 509 510
    # Here we obtain the wavelength beforehand to avoid reading it each time.
    datafile = open(dl2007_dir + "U{}/U{}_{}_MW3.1_{}.txt".format(umaximum[0],
                                                                  umaximum[0],
                                                                  umaximum[0],
                                                                  "00"))
    data = "".join(datafile.readlines()[-1001:])
    datafile.close()

    wave = np.genfromtxt(io.BytesIO(data.encode()), usecols=(0))
    # For some reason wavelengths are decreasing in the model files
    wave = wave[::-1]
    # We convert wavelengths from μm to nm
    wave *= 1000.

511 512
    # Conversion factor from Jy cm² sr¯¹ H¯¹ to W nm¯¹ (kg of H)¯¹
    conv = 4. * np.pi * 1e-30 / (cst.m_p+cst.m_e) * cst.c / (wave*wave) * 1e9
513 514 515 516 517 518 519 520 521 522 523 524 525 526

    for model in sorted(qpah.keys()):
        for umin in uminimum:
            filename = dl2007_dir + "U{}/U{}_{}_MW3.1_{}.txt".format(umin,
                                                                     umin,
                                                                     umin,
                                                                     model)
            print("Importing {}...".format(filename))
            datafile = open(filename)
            data = "".join(datafile.readlines()[-1001:])
            datafile.close()
            lumin = np.genfromtxt(io.BytesIO(data.encode()), usecols=(2))
            # For some reason fluxes are decreasing in the model files
            lumin = lumin[::-1]
527 528
            # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
            lumin *= conv/MdMH[model]
529

530
            models.append(DL2007(qpah[model], umin, umin, wave, lumin))
531 532 533 534 535 536 537 538 539 540 541 542 543
            for umax in umaximum:
                filename = dl2007_dir + "U{}/U{}_{}_MW3.1_{}.txt".format(umin,
                                                                         umin,
                                                                         umax,
                                                                         model)
                print("Importing {}...".format(filename))
                datafile = open(filename)
                data = "".join(datafile.readlines()[-1001:])
                datafile.close()
                lumin = np.genfromtxt(io.BytesIO(data.encode()), usecols=(2))
                # For some reason fluxes are decreasing in the model files
                lumin = lumin[::-1]

544 545
                # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
                lumin *= conv/MdMH[model]
546

547 548
                models.append(DL2007(qpah[model], umin, umax, wave, lumin))
    base.add_dl2007(models)
549 550


551
def build_dl2014(base):
552
    models = []
553 554
    dl2014_dir = os.path.join(os.path.dirname(__file__), 'dl2014/')

Médéric Boquien's avatar
Médéric Boquien committed
555 556 557
    qpah = {"000": 0.47, "010": 1.12, "020": 1.77, "030": 2.50, "040": 3.19,
            "050": 3.90, "060": 4.58, "070": 5.26, "080": 5.95, "090": 6.63,
            "100": 7.32}
558 559 560 561 562 563 564

    uminimum = ["0.100", "0.120", "0.150", "0.170", "0.200", "0.250", "0.300",
                "0.350", "0.400", "0.500", "0.600", "0.700", "0.800", "1.000",
                "1.200", "1.500", "1.700", "2.000", "2.500", "3.000", "3.500",
                "4.000", "5.000", "6.000", "7.000", "8.000", "10.00", "12.00",
                "15.00", "17.00", "20.00", "25.00", "30.00", "35.00", "40.00",
                "50.00"]
565

566 567 568 569
    alpha = ["1.0", "1.1", "1.2", "1.3", "1.4", "1.5", "1.6", "1.7", "1.8",
             "1.9", "2.0", "2.1", "2.2", "2.3", "2.4", "2.5", "2.6", "2.7",
             "2.8", "2.9", "3.0"]

570
    # Mdust/MH used to retrieve the dust mass as models as given per atom of H
Médéric Boquien's avatar
Médéric Boquien committed
571 572 573
    MdMH = {"000": 0.0100, "010": 0.0100, "020": 0.0101, "030": 0.0102,
            "040": 0.0102, "050": 0.0103, "060": 0.0104, "070": 0.0105,
            "080": 0.0106, "090": 0.0107, "100": 0.0108}
574

575 576 577 578 579 580 581 582 583 584 585 586 587
    # Here we obtain the wavelength beforehand to avoid reading it each time.
    datafile = open(dl2014_dir + "U{}_{}_MW3.1_{}/spec_1.0.dat"
                    .format(uminimum[0], uminimum[0], "000"))

    data = "".join(datafile.readlines()[-1001:])
    datafile.close()

    wave = np.genfromtxt(io.BytesIO(data.encode()), usecols=(0))
    # For some reason wavelengths are decreasing in the model files
    wave = wave[::-1]
    # We convert wavelengths from μm to nm
    wave *= 1000.

588 589
    # Conversion factor from Jy cm² sr¯¹ H¯¹ to W nm¯¹ (kg of H)¯¹
    conv = 4. * np.pi * 1e-30 / (cst.m_p+cst.m_e) * cst.c / (wave*wave) * 1e9
590 591 592 593 594 595 596 597 598 599 600 601

    for model in sorted(qpah.keys()):
        for umin in uminimum:
            filename = (dl2014_dir + "U{}_{}_MW3.1_{}/spec_1.0.dat"
                        .format(umin, umin, model))
            print("Importing {}...".format(filename))
            with open(filename) as datafile:
                data = "".join(datafile.readlines()[-1001:])
            lumin = np.genfromtxt(io.BytesIO(data.encode()), usecols=(2))
            # For some reason fluxes are decreasing in the model files
            lumin = lumin[::-1]

602 603
            # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
            lumin *= conv/MdMH[model]
604

605
            models.append(DL2014(qpah[model], umin, umin, 1.0, wave, lumin))
606 607 608 609 610 611 612 613 614 615
            for al in alpha:
                filename = (dl2014_dir + "U{}_1e7_MW3.1_{}/spec_{}.dat"
                            .format(umin, model, al))
                print("Importing {}...".format(filename))
                with open(filename) as datafile:
                    data = "".join(datafile.readlines()[-1001:])
                lumin = np.genfromtxt(io.BytesIO(data.encode()), usecols=(2))
                # For some reason fluxes are decreasing in the model files
                lumin = lumin[::-1]

616 617
                # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
                lumin *= conv/MdMH[model]
618

619
                models.append(DL2014(qpah[model], umin, 1e7, al, wave, lumin))
620

621
    base.add_dl2014(models)
622

623
def build_fritz2006(base):
624
    models = []
625
    fritz2006_dir = os.path.join(os.path.dirname(__file__), 'fritz2006/')
626

627 628
    # Parameters of Fritz+2006
    psy = [0.001, 10.100, 20.100, 30.100, 40.100, 50.100, 60.100, 70.100,
629 630
           80.100, 89.990]  # Viewing angle in degrees
    opening_angle = ["20", "40", "60"]  # Theta = 2*(90 - opening_angle)
631 632 633
    gamma = ["0.0", "2.0", "4.0", "6.0"]
    beta = ["-1.00", "-0.75", "-0.50", "-0.25", "0.00"]
    tau = ["0.1", "0.3", "0.6", "1.0", "2.0", "3.0", "6.0", "10.0"]
634
    r_ratio = ["10", "30", "60", "100", "150"]
635 636

    # Read and convert the wavelength
637 638 639
    datafile = open(fritz2006_dir + "ct{}al{}be{}ta{}rm{}.tot"
                    .format(opening_angle[0], gamma[0], beta[0], tau[0],
                            r_ratio[0]))
640 641 642 643
    data = "".join(datafile.readlines()[-178:])
    datafile.close()
    wave = np.genfromtxt(io.BytesIO(data.encode()), usecols=(0))
    wave *= 1e3
Médéric Boquien's avatar
Médéric Boquien committed
644
    # Number of wavelengths: 178; Number of comments lines: 28
645 646 647
    nskip = 28
    blocksize = 178

648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680
    iter_params = ((oa, gam, be, ta, rm)
                   for oa in opening_angle
                   for gam in gamma
                   for be in beta
                   for ta in tau
                   for rm in r_ratio)

    for params in iter_params:
        filename = fritz2006_dir + "ct{}al{}be{}ta{}rm{}.tot".format(*params)
        print("Importing {}...".format(filename))
        try:
            datafile = open(filename)
        except IOError:
            continue
        data = datafile.readlines()
        datafile.close()

        for n in range(len(psy)):
            block = data[nskip + blocksize * n + 4 * (n + 1) - 1:
                         nskip + blocksize * (n+1) + 4 * (n + 1) - 1]
            lumin_therm, lumin_scatt, lumin_agn = np.genfromtxt(
                io.BytesIO("".join(block).encode()), usecols=(2, 3, 4),
                unpack=True)
            # Remove NaN
            lumin_therm = np.nan_to_num(lumin_therm)
            lumin_scatt = np.nan_to_num(lumin_scatt)
            lumin_agn = np.nan_to_num(lumin_agn)
            # Conversion from erg/s/microns to W/nm
            lumin_therm *= 1e-4
            lumin_scatt *= 1e-4
            lumin_agn *= 1e-4
            # Normalization of the lumin_therm to 1W
            norm = np.trapz(lumin_therm, x=wave)
681 682 683
            lumin_therm /= norm
            lumin_scatt /= norm
            lumin_agn /= norm
684

685
            models.append(Fritz2006(params[4], params[3], params[2],
686
                                         params[1], params[0], psy[n], wave,
Médéric Boquien's avatar
Médéric Boquien committed
687
                                         lumin_therm, lumin_scatt, lumin_agn))
688

689
    base.add_fritz2006(models)
690

691
def build_nebular(base):
692 693
    models_lines = []
    models_cont = []
694

695 696 697
    nebular_dir = os.path.join(os.path.dirname(__file__), 'nebular/')
    print("Importing {}...".format(nebular_dir + 'lines.dat'))
    lines = np.genfromtxt(nebular_dir + 'lines.dat')
698

699 700 701
    wave_lines = np.genfromtxt(nebular_dir + 'line_wavelengths.dat')
    print("Importing {}...".format(nebular_dir + 'continuum.dat'))
    cont = np.genfromtxt(nebular_dir + 'continuum.dat')
702

703 704 705
    # Convert wavelength from Å to nm
    wave_lines *= 0.1
    wave_cont = cont[:1600, 0] * 0.1
706

707 708
    # Get the list of metallicities
    metallicities = np.unique(lines[:, 1])
709

710 711 712
    # Keep only the fluxes
    lines = lines[:, 2:]
    cont = cont[:, 1:]
713

714 715 716
    # We select only models with ne=100. Other values could be included later
    lines = lines[:, 1::3]
    cont = cont[:, 1::3]
717

718 719
    # Convert lines to W and to a linear scale
    lines = 10**(lines-7)
720

721 722 723
    # Convert continuum to W/nm
    cont *= np.tile(1e-7 * cst.c * 1e9 / wave_cont**2,
                    metallicities.size)[:, np.newaxis]
724

725 726 727 728 729 730 731
    # Import lines
    for idx, metallicity in enumerate(metallicities):
        spectra = lines[idx::6, :]
        for logU, spectrum in zip(np.around(np.arange(-4., -.9, .1), 1),
                                  spectra.T):
            models_lines.append(NebularLines(metallicity, logU, wave_lines,
                                             spectrum))
732

733 734 735 736 737 738 739
    # Import continuum
    for idx, metallicity in enumerate(metallicities):
        spectra = cont[1600 * idx: 1600 * (idx+1), :]
        for logU, spectrum in zip(np.around(np.arange(-4., -.9, .1), 1),
                                  spectra.T):
            models_cont.append(NebularContinuum(metallicity, logU, wave_cont,
                                                spectrum))
740

741
    base.add_nebular_lines(models_lines)
742
    base.add_nebular_continuum(models_cont)
743

744

745 746
def build_schreiber2016(base):
    models = []
747 748 749 750
    schreiber2016_dir = os.path.join(os.path.dirname(__file__),
                                     'schreiber2016/')

    print("Importing {}...".format(schreiber2016_dir + 'g15_pah.fits'))
751
    pah = Table.read(schreiber2016_dir + 'g15_pah.fits')
752
    print("Importing {}...".format(schreiber2016_dir + 'g15_dust.fits'))
753 754
    dust = Table.read(schreiber2016_dir + 'g15_dust.fits')

755
    # Getting the lambda grid for the templates and convert from μm to nm.
756
    wave = dust['LAM'][0, 0, :].data * 1e3
757 758

    for td in np.arange(15., 100.):
759
        # Find the closest temperature in the model list of tdust
760
        tsed = np.argmin(np.absolute(dust['TDUST'][0].data-td))
761 762

        # The models are in νFν.  We convert this to W/nm.
763 764
        lumin_dust = dust['SED'][0, tsed, :].data / wave
        lumin_pah = pah['SED'][0, tsed, :].data / wave
765 766 767 768

        models.append(Schreiber2016(0, td, wave, lumin_dust))
        models.append(Schreiber2016(1, td, wave, lumin_pah))

769 770
    base.add_schreiber2016(models)

771

772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841
def build_themis(base):
    models = []
    themis_dir = os.path.join(os.path.dirname(__file__), 'themis/')

    # Mass fraction of hydrocarbon solids i.e., a-C(:H) smaller than 1.5 nm,
    # also known as HAC
    qhac = {"000": 0.02, "010": 0.06, "020": 0.10, "030": 0.14, "040": 0.17,
            "050": 0.20, "060": 0.24, "070": 0.28, "080": 0.32, "090": 0.36,
            "100": 0.40}

    uminimum = ["0.100", "0.120", "0.150", "0.170", "0.200", "0.250", "0.300",
                "0.350", "0.400", "0.500", "0.600", "0.700", "0.800", "1.000",
                "1.200", "1.500", "1.700", "2.000", "2.500", "3.000", "3.500",
                "4.000", "5.000", "6.000", "7.000", "8.000", "10.00", "12.00",
                "15.00", "17.00", "20.00", "25.00", "30.00", "35.00", "40.00",
                "50.00", "80.00"]

    alpha = ["1.0", "1.1", "1.2", "1.3", "1.4", "1.5", "1.6", "1.7", "1.8",
             "1.9", "2.0", "2.1", "2.2", "2.3", "2.4", "2.5", "2.6", "2.7",
             "2.8", "2.9", "3.0"]

    # Mdust/MH used to retrieve the dust mass as models as given per atom of H
    MdMH = {"000": 7.4e-3, "010": 7.4e-3, "020": 7.4e-3, "030": 7.4e-3,
            "040": 7.4e-3, "050": 7.4e-3, "060": 7.4e-3, "070": 7.4e-3,
            "080": 7.4e-3, "090": 7.4e-3, "100": 7.4e-3}

    # Here we obtain the wavelength beforehand to avoid reading it each time.
    datafile = open(themis_dir + "U{}_{}_MW3.1_{}/spec_1.0.dat"
                    .format(uminimum[0], uminimum[0], "000"))

    data = "".join(datafile.readlines()[-576:])
    datafile.close()

    wave = np.genfromtxt(io.BytesIO(data.encode()), usecols=(0))

    # We convert wavelengths from μm to nm
    wave *= 1000.

    # Conversion factor from Jy cm² sr¯¹ H¯¹ to W nm¯¹ (kg of H)¯¹
    conv = 4. * np.pi * 1e-30 / (cst.m_p+cst.m_e) * cst.c / (wave*wave) * 1e9

    for model in sorted(qhac.keys()):
        for umin in uminimum:
            filename = (themis_dir + "U{}_{}_MW3.1_{}/spec_1.0.dat"
                        .format(umin, umin, model))
            print("Importing {}...".format(filename))
            with open(filename) as datafile:
                data = "".join(datafile.readlines()[-576:])
            lumin = np.genfromtxt(io.BytesIO(data.encode()), usecols=(2))

            # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
            lumin *= conv / MdMH[model]

            models.append(THEMIS(qhac[model], umin, umin, 1.0, wave, lumin))
            for al in alpha:
                filename = (themis_dir + "U{}_1e7_MW3.1_{}/spec_{}.dat"
                            .format(umin, model, al))
                print("Importing {}...".format(filename))
                with open(filename) as datafile:
                    data = "".join(datafile.readlines()[-576:])
                lumin = np.genfromtxt(io.BytesIO(data.encode()), usecols=(2))

                # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
                lumin *= conv/MdMH[model]

                models.append(THEMIS(qhac[model], umin, 1e7, al, wave, lumin))

    base.add_themis(models)


842
def build_base(bc03res='lr'):
843 844 845 846 847 848
    base = Database(writable=True)
    base.upgrade_base()

    print('#' * 78)
    print("1- Importing filters...\n")
    build_filters(base)
Médéric Boquien's avatar
Médéric Boquien committed
849
    build_filters_gazpar(base)
850 851 852 853 854 855 856 857 858
    print("\nDONE\n")
    print('#' * 78)

    print("2- Importing Maraston 2005 SSP\n")
    build_m2005(base)
    print("\nDONE\n")
    print('#' * 78)

    print("3- Importing Bruzual and Charlot 2003 SSP\n")
859
    build_bc2003(base, bc03res)
860 861 862
    print("\nDONE\n")
    print('#' * 78)

863
    print("4- Importing Draine and Li (2007) models\n")
864 865 866 867
    build_dl2007(base)
    print("\nDONE\n")
    print('#' * 78)

868
    print("5- Importing the updated Draine and Li (2007 models)\n")
869 870 871 872
    build_dl2014(base)
    print("\nDONE\n")
    print('#' * 78)

873
    print("6- Importing Fritz et al. (2006) models\n")
874
    build_fritz2006(base)
Yannick Roehlly's avatar
Yannick Roehlly committed
875 876 877
    print("\nDONE\n")
    print('#' * 78)

878
    print("7- Importing Dale et al (2014) templates\n")
879 880 881
    build_dale2014(base)
    print("\nDONE\n")
    print('#' * 78)
Médéric Boquien's avatar
Médéric Boquien committed
882

883
    print("8- Importing nebular lines and continuum\n")
884
    build_nebular(base)
885 886
    print("\nDONE\n")
    print('#' * 78)
887

888 889 890 891
    print("9- Importing Schreiber et al (2016) models\n")
    build_schreiber2016(base)
    print("\nDONE\n")
    print('#' * 78)
892 893 894 895 896 897

    print("10- Importing Jones et al (2017) models)\n")
    build_themis(base)
    print("\nDONE\n")
    print('#' * 78)

898 899
    base.session.close_all()

Yannick Roehlly's avatar
Yannick Roehlly committed
900 901 902

if __name__ == '__main__':
    build_base()