__init__.py 39.4 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, BC03_SSP, Fritz2006,
24
                          Dale2014, DL2007, DL2014, NebularLines,
25 26
                          NebularContinuum, Schreiber2016, THEMIS,
                          Yggdrasil_SSP)
Yannick Roehlly's avatar
Yannick Roehlly committed
27 28


29 30 31 32 33
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
34
    separated by a space (or a carriage return). There are the time vector, 5
35 36 37 38 39 40 41 42 43 44 45 46
    (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
47
              Vector of the time grid of the SSP in Myr.
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 74
    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.
75
    what_line = next(file_structure)
76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
    # 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:
118
                what_line = next(file_structure)
119

Yannick Roehlly's avatar
Yannick Roehlly committed
120
    # The time grid is in year, we want Myr.
121
    time_grid = np.array(time_grid, dtype=float)
122
    time_grid *= 1.e-6
123 124 125 126

    # 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)
127
    wavelength *= 0.1
128 129 130 131

    # 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)
132
    luminosity *= 3.826e27
133 134 135 136 137 138 139 140
    # 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:]


141
def build_filters(base):
142
    filters = []
143
    filters_dir = os.path.join(os.path.dirname(__file__), 'filters/')
144
    for filter_file in glob.glob(filters_dir + '**/*.dat', recursive=True):
Yannick Roehlly's avatar
Yannick Roehlly committed
145 146 147 148
        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')
149 150 151 152 153

        # Make the name dynamic for filters in subdirectories
        tmp_name = filter_file.replace(filters_dir, '')[:-4]
        if '/' in tmp_name:
            filter_name = tmp_name.replace('/', '.')
Yannick Roehlly's avatar
Yannick Roehlly committed
154 155 156 157
        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()
158

Yannick Roehlly's avatar
Yannick Roehlly committed
159 160 161
        # We convert the wavelength from Å to nm.
        filter_table[0] *= 0.1

162 163 164 165 166 167 168
        # 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
169 170 171
        print("Importing %s... (%s points)" % (filter_name,
                                               filter_table.shape[1]))

172
        new_filter = Filter(filter_name, filter_description, filter_table)
Yannick Roehlly's avatar
Yannick Roehlly committed
173

174 175 176
        # 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.
177 178
        if not (filter_name.startswith('PSEUDO') or
                filter_name.startswith('linefilter')):
179 180
            new_filter.normalise()
        else:
181
            new_filter.pivot_wavelength = np.mean(
182 183
                filter_table[0][filter_table[1] > 0]
            )
184
        filters.append(new_filter)
Yannick Roehlly's avatar
Yannick Roehlly committed
185

186
    base.add_filters(filters)
Yannick Roehlly's avatar
Yannick Roehlly committed
187

Médéric Boquien's avatar
Médéric Boquien committed
188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204
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()
205

Médéric Boquien's avatar
Médéric Boquien committed
206 207 208
        # We convert the wavelength from Å to nm.
        filter_table[0] *= 0.1

209 210 211 212 213 214 215
        # 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
216 217 218
        print("Importing %s... (%s points)" % (filter_name,
                                               filter_table.shape[1]))

219
        new_filter = Filter(filter_name, filter_desc, filter_table)
Médéric Boquien's avatar
Médéric Boquien committed
220

221 222 223
        # 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
224 225 226
        if not filter_name.startswith('PSEUDO'):
            new_filter.normalise()
        else:
227
            new_filter.pivot_wavelength = np.mean(
Médéric Boquien's avatar
Médéric Boquien committed
228 229 230 231 232
                filter_table[0][filter_table[1] > 0]
            )
        filters.append(new_filter)

    base.add_filters(filters)
233 234 235

def build_m2005(base):
    m2005_dir = os.path.join(os.path.dirname(__file__), 'maraston2005/')
Yannick Roehlly's avatar
Yannick Roehlly committed
236

Yannick Roehlly's avatar
Yannick Roehlly committed
237
    # Age grid (1 Myr to 13.7 Gyr with 1 Myr step)
238 239
    time_grid = np.arange(1, 13701)
    fine_time_grid = np.linspace(0.1, 13700, 137000)
Yannick Roehlly's avatar
Yannick Roehlly committed
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254

    # 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:
255
            imf = 'krou'
Yannick Roehlly's avatar
Yannick Roehlly committed
256 257
            mass_table = np.copy(kroupa_mass)
        elif 'ssz' in spec_file:
258
            imf = 'salp'
Yannick Roehlly's avatar
Yannick Roehlly committed
259 260 261 262 263
            mass_table = np.copy(salpeter_mass)
        else:
            raise ValueError('Unknown IMF!!!')

        # Keep only the actual metallicity values in the mass table
264 265 266 267
        # 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
268

269 270 271 272 273 274 275 276
        # 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
277

278 279 280
        # 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
281 282

        # Remove the metallicity column from the spec table
283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303
        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)
304

305 306 307
        # 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
308
        ssp_wave_resamp = np.around(np.logspace(np.log10(10000),
309
                                                   np.log10(160000), 50))
310 311 312 313
        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:, :]),
314
                                    assume_sorted=True,
315
                                    axis=0)(np.log10(ssp_wave_resamp))
316

317 318 319
        ssp_wave = np.hstack([ssp_wave[:argmin+1], ssp_wave_resamp])
        ssp_lumin = np.vstack([ssp_lumin_interp[:argmin+1, :],
                               ssp_lumin_resamp])
320

321 322 323 324 325
        # 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
326

327 328
        base.add_m2005(M2005(imf, metallicity, time_grid, ssp_wave,
                             mass_table, ssp_lumin))
Yannick Roehlly's avatar
Yannick Roehlly committed
329 330


331 332
def build_bc2003(base, res):
    bc03_dir = os.path.join(os.path.dirname(__file__), 'bc03/')
333

334 335
    # Time grid (1 Myr to 14 Gyr with 1 Myr step)
    time_grid = np.arange(1, 14000)
336
    fine_time_grid = np.linspace(0.1, 13999, 139990)
337 338 339 340 341 342 343 344 345 346 347 348

    # 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"]):
349 350 351 352 353 354
        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)
355

356
        print("Importing {}...".format(ssp_filename))
357 358 359 360 361

        # Read the desired information from the color files
        color_table = []
        color3_table = np.genfromtxt(color3_filename).transpose()
        color4_table = np.genfromtxt(color4_filename).transpose()
362 363 364
        color_table.append(color4_table[6])        # Mstar
        color_table.append(color4_table[7])        # Mgas
        color_table.append(10 ** color3_table[5])  # NLy
365 366 367 368 369

        color_table = np.array(color_table)

        ssp_time, ssp_wave, ssp_lumin = read_bc03_ssp(ssp_filename)

370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392
        # 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)
393

394 395 396 397 398 399 400 401
        # 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:]),
402
                                    np.log10(ssp_lumin_interp[argmin:, :]),
403 404 405 406
                                    assume_sorted=True,
                                    axis=0)(np.log10(ssp_wave_resamp))

        ssp_wave = np.hstack([ssp_wave[:argmin+1], ssp_wave_resamp])
407 408
        ssp_lumin = np.vstack([ssp_lumin_interp[:argmin+1, :],
                               ssp_lumin_resamp])
409

410
        base.add_bc03(BC03(
411 412 413 414 415 416 417 418
            imf,
            metallicity[key],
            time_grid,
            ssp_wave,
            color_table,
            ssp_lumin
        ))

419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460
def build_bc2003_ssp(base, res):
    bc03_dir = os.path.join(os.path.dirname(__file__), 'bc03/')

    # 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"]):
        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)

        print("Importing {}...".format(ssp_filename))

        # Read the desired information from the color files
        color_table = []
        color3_table = np.genfromtxt(color3_filename).transpose()
        color4_table = np.genfromtxt(color4_filename).transpose()
        color_table.append(color4_table[6])        # Mstar
        color_table.append(color4_table[7])        # Mgas
        color_table.append(10 ** color3_table[5])  # NLy
        color_table = np.array(color_table)

        ssp_time, ssp_wave, ssp_lumin = read_bc03_ssp(ssp_filename)

        base.add_bc03_ssp(BC03_SSP(
            imf,
            metallicity[key],
            ssp_time,
            ssp_wave,
            color_table,
            ssp_lumin
        ))
461

462 463 464 465 466
def build_yggdrasil_ssp(base):
    yggdrasil_dir = os.path.join(os.path.dirname(__file__), 'yggdrasil/')

    # Metallicities associated to each key
    metallicities = ["0.004", "0.008", "0.02"]
467
    fcovs = ["0", "0.5"]
468

469 470
    for Z, fcov in itertools.product(metallicities, fcovs):
        filename = f"{yggdrasil_dir}Z={Z}_kroupa_IMF_fcov_{fcov}_SFR_inst_Spectra"
471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495
        print(f"Importing {filename}...")

        with open(filename) as f:
            specallages = "".join(f.readlines()[2::]).split('\n\n')[:-1]
        for i in range(len(specallages)):
            specallages[i] = specallages[i].split('\n')

        ssp_time = np.array([float(item[0].split()[-1]) for item in specallages])
        ssp_info = np.array([float(item[2].split()[-1]) for item in specallages])

        # Normalisation from 10⁶ to 1 Msun
        ssp_info *= 1e-6

        minwlsize = int(np.min([float(item[3].split()[-1]) for item in specallages]))
        ssp_wave = np.array([float(line.split(' ')[0]) for line in specallages[0][7:]])[-minwlsize:]

        # Conversion from Å to nm
        ssp_wave *= .1

        ssp_lumin = np.empty((minwlsize, ssp_time.size))
        for i, spec in enumerate(specallages):
            ssp_lumin[:, i] = np.array([float(line.split(' ')[-1]) for line in spec[7:]])[-minwlsize:]

        # Conversion from erg/s/Å/10⁶ Msun to W/nm/Msun
        ssp_lumin *= 1e-7 * 10 * 1e-6
496 497
        base.add_yggdrasil_ssp(Yggdrasil_SSP(float(Z), float(fcov), ssp_time,
                                             ssp_wave, ssp_info, ssp_lumin))
498

499
def build_dale2014(base):
500
    models = []
501 502 503
    dale2014_dir = os.path.join(os.path.dirname(__file__), 'dale2014/')

    # Getting the alpha grid for the templates
504
    d14cal = np.genfromtxt(dale2014_dir + 'dhcal.dat')
505 506 507
    alpha_grid = d14cal[:, 1]

    # Getting the lambda grid for the templates and convert from microns to nm.
508
    first_template = np.genfromtxt(dale2014_dir + 'spectra.0.00AGN.dat')
509 510
    wave = first_template[:, 0] * 1E3

Médéric Boquien's avatar
Médéric Boquien committed
511 512 513 514
    # Getting the stellar emission and interpolate it at the same wavelength
    # grid
    stell_emission_file = np.genfromtxt(dale2014_dir +
                                        'stellar_SED_age13Gyr_tau10Gyr.spec')
515
    # A -> to nm
Médéric Boquien's avatar
Médéric Boquien committed
516
    wave_stell = stell_emission_file[:, 0] * 0.1
517
    # W/A -> W/nm
Médéric Boquien's avatar
Médéric Boquien committed
518 519
    stell_emission = stell_emission_file[:, 1] * 10
    stell_emission_interp = np.interp(wave, wave_stell, stell_emission)
520 521 522 523 524 525 526 527 528 529 530 531

    # 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()

532
    for al in range(1, len(alpha_grid)+1, 1):
Médéric Boquien's avatar
Médéric Boquien committed
533 534 535
        lumin_with_stell = np.genfromtxt(io.BytesIO(data.encode()),
                                         usecols=(al))
        lumin_with_stell = pow(10, lumin_with_stell) / wave
536 537
        constant = lumin_with_stell[7] / stell_emission_interp[7]
        lumin = lumin_with_stell - stell_emission_interp * constant
Médéric Boquien's avatar
Médéric Boquien committed
538 539 540
        lumin[lumin < 0] = 0
        lumin[wave < 2E3] = 0
        norm = np.trapz(lumin, x=wave)
541
        lumin /= norm
542

543
        models.append(Dale2014(fraction, alpha_grid[al-1], wave, lumin))
544
    # Emission from dust heated by AGN - Quasar template
545
    filename = dale2014_dir + "shi_agn.regridded.extended.dat"
546 547
    print("Importing {}...".format(filename))

548 549 550 551
    wave, lumin_quasar = np.genfromtxt(filename, unpack=True)
    wave *= 1e3
    lumin_quasar = 10**lumin_quasar / wave
    norm = np.trapz(lumin_quasar, x=wave)
552
    lumin_quasar /= norm
553

554 555 556
    models.append(Dale2014(1.0, 0.0, wave, lumin_quasar))

    base.add_dale2014(models)
557

558

559
def build_dl2007(base):
560
    models = []
561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578
    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"]

579
    # Mdust/MH used to retrieve the dust mass as models as given per atom of H
Médéric Boquien's avatar
Médéric Boquien committed
580 581
    MdMH = {"00": 0.0100, "10": 0.0100, "20": 0.0101, "30": 0.0102,
            "40": 0.0102, "50": 0.0103, "60": 0.0104}
582

583 584 585 586 587 588 589 590 591 592 593 594 595 596
    # 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.

597 598
    # 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
599 600 601 602 603 604 605 606 607 608 609 610 611 612

    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]
613 614
            # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
            lumin *= conv/MdMH[model]
615

616
            models.append(DL2007(qpah[model], umin, umin, wave, lumin))
617 618 619 620 621 622 623 624 625 626 627 628 629
            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]

630 631
                # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
                lumin *= conv/MdMH[model]
632

633 634
                models.append(DL2007(qpah[model], umin, umax, wave, lumin))
    base.add_dl2007(models)
635 636


637
def build_dl2014(base):
638
    models = []
639 640
    dl2014_dir = os.path.join(os.path.dirname(__file__), 'dl2014/')

Médéric Boquien's avatar
Médéric Boquien committed
641 642 643
    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}
644 645 646 647 648 649 650

    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"]
651

652 653 654 655
    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"]

656
    # Mdust/MH used to retrieve the dust mass as models as given per atom of H
Médéric Boquien's avatar
Médéric Boquien committed
657 658 659
    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}
660

661 662 663 664 665 666 667 668 669 670 671 672 673
    # 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.

674 675
    # 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
676 677 678 679 680 681 682 683 684 685 686 687

    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]

688 689
            # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
            lumin *= conv/MdMH[model]
690

691
            models.append(DL2014(qpah[model], umin, umin, 1.0, wave, lumin))
692 693 694 695 696 697 698 699 700 701
            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]

702 703
                # Conversion from Jy cm² sr¯¹ H¯¹to W nm¯¹ (kg of dust)¯¹
                lumin *= conv/MdMH[model]
704

705
                models.append(DL2014(qpah[model], umin, 1e7, al, wave, lumin))
706

707
    base.add_dl2014(models)
708

709
def build_fritz2006(base):
710
    models = []
711
    fritz2006_dir = os.path.join(os.path.dirname(__file__), 'fritz2006/')
712

713 714
    # Parameters of Fritz+2006
    psy = [0.001, 10.100, 20.100, 30.100, 40.100, 50.100, 60.100, 70.100,
715 716
           80.100, 89.990]  # Viewing angle in degrees
    opening_angle = ["20", "40", "60"]  # Theta = 2*(90 - opening_angle)
717 718 719
    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"]
720
    r_ratio = ["10", "30", "60", "100", "150"]
721 722

    # Read and convert the wavelength
723 724 725
    datafile = open(fritz2006_dir + "ct{}al{}be{}ta{}rm{}.tot"
                    .format(opening_angle[0], gamma[0], beta[0], tau[0],
                            r_ratio[0]))
726 727 728 729
    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
730
    # Number of wavelengths: 178; Number of comments lines: 28
731 732 733
    nskip = 28
    blocksize = 178

734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766
    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)
767 768 769
            lumin_therm /= norm
            lumin_scatt /= norm
            lumin_agn /= norm
770

771
            models.append(Fritz2006(params[4], params[3], params[2],
772
                                         params[1], params[0], psy[n], wave,
Médéric Boquien's avatar
Médéric Boquien committed
773
                                         lumin_therm, lumin_scatt, lumin_agn))
774

775
    base.add_fritz2006(models)
776

777
def build_nebular(base):
778 779
    models_lines = []
    models_cont = []
780

781 782 783
    nebular_dir = os.path.join(os.path.dirname(__file__), 'nebular/')
    print("Importing {}...".format(nebular_dir + 'lines.dat'))
    lines = np.genfromtxt(nebular_dir + 'lines.dat')
784

785 786 787 788
    tmp = Table.read(nebular_dir + 'line_wavelengths.dat', format='ascii')
    wave_lines = tmp['col1'].data
    name_lines = tmp['col2'].data

789 790
    print("Importing {}...".format(nebular_dir + 'continuum.dat'))
    cont = np.genfromtxt(nebular_dir + 'continuum.dat')
791

792 793 794
    # Convert wavelength from Å to nm
    wave_lines *= 0.1
    wave_cont = cont[:1600, 0] * 0.1
795

796 797
    # Get the list of metallicities
    metallicities = np.unique(lines[:, 1])
798

799 800 801
    # Keep only the fluxes
    lines = lines[:, 2:]
    cont = cont[:, 1:]
802

803 804 805
    # We select only models with ne=100. Other values could be included later
    lines = lines[:, 1::3]
    cont = cont[:, 1::3]
806

807 808
    # Convert lines to W and to a linear scale
    lines = 10**(lines-7)
809

810 811 812
    # Convert continuum to W/nm
    cont *= np.tile(1e-7 * cst.c * 1e9 / wave_cont**2,
                    metallicities.size)[:, np.newaxis]
813

814 815 816 817 818
    # 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):
819 820
            models_lines.append(NebularLines(metallicity, logU, name_lines,
                                             wave_lines, spectrum))
821

822 823 824 825 826 827 828
    # 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))
829

830
    base.add_nebular_lines(models_lines)
831
    base.add_nebular_continuum(models_cont)
832

833

834 835
def build_schreiber2016(base):
    models = []
836 837 838 839
    schreiber2016_dir = os.path.join(os.path.dirname(__file__),
                                     'schreiber2016/')

    print("Importing {}...".format(schreiber2016_dir + 'g15_pah.fits'))
840
    pah = Table.read(schreiber2016_dir + 'g15_pah.fits')
841
    print("Importing {}...".format(schreiber2016_dir + 'g15_dust.fits'))
842 843
    dust = Table.read(schreiber2016_dir + 'g15_dust.fits')

844
    # Getting the lambda grid for the templates and convert from μm to nm.
845
    wave = dust['LAM'][0, 0, :].data * 1e3
846 847

    for td in np.arange(15., 100.):
848
        # Find the closest temperature in the model list of tdust
849
        tsed = np.argmin(np.absolute(dust['TDUST'][0].data-td))
850 851

        # The models are in νFν.  We convert this to W/nm.
852 853
        lumin_dust = dust['SED'][0, tsed, :].data / wave
        lumin_pah = pah['SED'][0, tsed, :].data / wave
854 855 856 857

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

858 859
    base.add_schreiber2016(models)

860

861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930
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)


931
def build_base(bc03res='lr'):
932 933 934 935 936 937
    base = Database(writable=True)
    base.upgrade_base()

    print('#' * 78)
    print("1- Importing filters...\n")
    build_filters(base)
Médéric Boquien's avatar
Médéric Boquien committed
938
    build_filters_gazpar(base)
939 940 941 942 943 944 945 946 947
    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")
948
    build_bc2003_ssp(base, bc03res)
949
    build_bc2003(base, bc03res)
950 951 952
    print("\nDONE\n")
    print('#' * 78)

953
    print("4- Importing Draine and Li (2007) models\n")
954 955 956 957
    build_dl2007(base)
    print("\nDONE\n")
    print('#' * 78)

958
    print("5- Importing the updated Draine and Li (2007 models)\n")
959 960 961 962
    build_dl2014(base)
    print("\nDONE\n")
    print('#' * 78)

963
    print("6- Importing Fritz et al. (2006) models\n")
964
    build_fritz2006(base)
Yannick Roehlly's avatar
Yannick Roehlly committed
965 966 967
    print("\nDONE\n")
    print('#' * 78)

968
    print("7- Importing Dale et al (2014) templates\n")
969 970 971
    build_dale2014(base)
    print("\nDONE\n")
    print('#' * 78)
Médéric Boquien's avatar
Médéric Boquien committed
972

973
    print("8- Importing nebular lines and continuum\n")
974
    build_nebular(base)
975 976
    print("\nDONE\n")
    print('#' * 78)
977

978 979 980 981
    print("9- Importing Schreiber et al (2016) models\n")
    build_schreiber2016(base)
    print("\nDONE\n")
    print('#' * 78)
982 983 984 985 986 987

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

Médéric Boquien's avatar
Médéric Boquien committed
988
    print("11- Importing the Yggdrasil SSP")
989
    build_yggdrasil_ssp(base)
990 991 992
    print("\nDONE\n")
    print('#' * 78)

993 994
    base.session.close_all()

Yannick Roehlly's avatar
Yannick Roehlly committed
995 996 997

if __name__ == '__main__':
    build_base()