# Copyright (c) 2016-2017 Weitian LI # MIT license """ Galactic supernova remnants (SNRs) emission simulations. References ---------- .. [Jelic2008] Jelić, V. et al., "Foreground simulations for the LOFAR-epoch of reionization experiment", 2008, MNRAS, 389, 1319-1335, http://adsabs.harvard.edu/abs/2008MNRAS.389.1319J .. [Green2014] Green, D. A., "A catalogue of 294 Galactic supernova remnants", 2014, Bulletin of the Astronomical Society of India, 42, 47-58, http://adsabs.harvard.edu/abs/2014BASI...42...47G .. [GreenSNRDataWeb] A Catalogue of Galactic Supernova Remnants http://www.mrao.cam.ac.uk/surveys/snrs/ """ import os import logging from datetime import datetime, timezone import numpy as np from astropy.io import fits from astropy.coordinates import SkyCoord import astropy.units as au import pandas as pd from ..sky import get_sky from ..utils.wcs import make_wcs from ..utils.convert import Fnu_to_Tb_fast from ..utils.grid import make_ellipse from ..utils.units import UnitConversions as AUC logger = logging.getLogger(__name__) class SuperNovaRemnants: """ Simulate the Galactic supernova remnants emission. The simulation follows the method adopted by [Jelic2008]_, which is based on the Galactic SNRs catalog maintained by *D. A. Green* [Green2014]_ and [GreenSNRDataWeb]_, which contains 294 SNRs (2014-May). However, some SNRs have incomplete data which are excluded, while some SNRs with uncertain properties are currently kept. Every SNR is simulated as an *ellipse* of *uniform brightness* on a local coordinate grid with relatively higher resolution compared to the output HEALPix map, which is then mapped to the output HEALPix map by down-sampling. Parameters ---------- configs : `ConfigManager` A `ConfigManager` instance containing default and user configurations. For more details, see the example configuration specifications. Attributes ---------- TODO """ # Component name name = "Galactic SNRs" def __init__(self, configs): self.configs = configs self.sky = get_sky(configs) self._set_configs() def _set_configs(self): """Load the configs and set the corresponding class attributes.""" comp = "galactic/snr" self.catalog_path = self.configs.get_path(comp+"/catalog") self.catalog_outfile = self.configs.get_path(comp+"/catalog_outfile") self.resolution = self.configs.getn(comp+"/resolution") # [ arcsec ] self.prefix = self.configs.getn(comp+"/prefix") self.save = self.configs.getn(comp+"/save") self.output_dir = self.configs.get_path(comp+"/output_dir") # self.filename_pattern = self.configs.getn("output/filename_pattern") self.use_float = self.configs.getn("output/use_float") self.checksum = self.configs.getn("output/checksum") self.clobber = self.configs.getn("output/clobber") self.frequencies = self.configs.frequencies # [MHz] self.freq_unit = au.Unit(self.configs.getn("frequency/unit")) logger.info("Loaded and set up configurations") def _load_catalog(self): """ Load the Galactic SNRs catalog data. Catalog columns: * glon, glat : SNR coordinate, Galactic coordinate, [deg] * size_major, size_minor : SNR angular sizes; major and minor axes of the ellipse fitted to the SNR, or the diameter of the fitted circle if the SNR is nearly circular; originally in [arcmin], converted to [arcsec] * flux : Flux density at 1 GHz, [Jy] """ self.catalog = pd.read_csv(self.catalog_path) nrow, ncol = self.catalog.shape logger.info("Loaded SNRs catalog data from: {0}".format( self.catalog_path)) logger.info("SNRs catalog data: {0} objects, {1} columns".format( nrow, ncol)) # The flux densities are given at 1 GHz self.catalog_flux_freq = (1.0*au.GHz).to(self.freq_unit).value # Convert ``size_major`` and ``size_minor`` from unit [arcmin] # to [arcsec] self.catalog["size_major"] *= AUC.arcmin2arcsec self.catalog["size_minor"] *= AUC.arcmin2arcsec def _save_catalog_inuse(self): """ Save the effective/inuse SNRs catalog data to a CSV file. NOTE ---- - Only the effective/inuse SNRs are saved (i.e., without the ones that are filtered out). - Also save the simulated rotation column. - The unnecessary columns are striped. """ if self.catalog_outfile is None: logger.warning("Catalog output file not set; skipped!") return # Create directory if necessary dirname = os.path.dirname(self.catalog_outfile) if not os.path.exists(dirname): os.mkdir(dirname) logger.info("Created directory: {0}".format(dirname)) # Save catalog data colnames = ["name", "glon", "glat", "ra", "dec", "size_major", "size_minor", "flux", "specindex", "rotation"] if os.path.exists(self.catalog_outfile): if self.clobber: os.remove(self.catalog_outfile) logger.warning("Removed existing catalog file: {0}".format( self.catalog_outfile)) else: raise OSError("Output file already exists: {0}".format( self.catalog_outfile)) self.catalog.to_csv(self.catalog_outfile, columns=colnames, header=True, index=False) logger.info("Saved SNRs catalog in use to: %s" % self.catalog_outfile) def _filter_catalog(self): """ Filter the catalog data to remove the objects with incomplete data, as well as the SNRs lying outside the sky coverage. The following cases are filtered out: - Missing angular size - Missing flux density data - Missing spectral index value NOTE ---- The objects with uncertain data are currently kept. """ cond1 = pd.isnull(self.catalog["size_major"]) cond2 = pd.isnull(self.catalog["size_minor"]) cond3 = pd.isnull(self.catalog["flux"]) cond4 = pd.isnull(self.catalog["specindex"]) cond_keep = ~(cond1 | cond2 | cond3 | cond4) n_remain = cond_keep.sum() n_delete = len(cond_keep) - n_remain self.catalog = self.catalog[cond_keep] logger.info("SNRs catalog: filtered out due to incomplete data: " + "{0:d} objects".format(n_delete)) # Filter out the SNRs lying outside the sky region (e.g., a patch) skycoords = SkyCoord(l=self.catalog["glon"], b=self.catalog["glat"], frame="galactic", unit="deg") inside = self.sky.contains(skycoords) n_remain = inside.sum() n_delete = len(inside) - n_remain self.catalog = self.catalog[inside] # Drop the index self.catalog.reset_index(drop=True, inplace=True) self.catalog_filtered = True logger.info("SNRs catalog: filtered out due to sky coverage: " + "{0:d} objects".format(n_delete)) logger.info("Filtered SNRs catalog: {0} objects".format(n_remain)) if n_remain == 0: raise RuntimeError("NO remaining SNRs within simulation sky! " + "Check the catalog or disable this component.") def _add_random_rotation(self): """ Add random rotation angles for each SNR as column "rotation" within the catalog data frame. The rotation angles are uniformly distributed within [0, 360). The rotation happens on the spherical surface, i.e., not with respect to the line of sight, but to the Galactic frame coordinate axes. """ num = len(self.catalog) angles = np.random.uniform(low=0.0, high=360.0, size=num) rotation = pd.Series(data=angles, name="rotation") self.catalog["rotation"] = rotation logger.info("Added random rotation angles as the 'rotation' column") def _calc_Tb(self, flux, specindex, frequency, size): """ Calculate the brightness temperature at requested frequency by assuming a power-law spectral shape. Parameters ---------- flux : float The flux density (unit: [ Jy ]) at the reference frequency (i.e., `self.catalog_flux_freq`). specindex : float The spectral index of the power-law spectrum frequency : float The frequency (unit: [ MHz ]) where the brightness temperature requested. size : 2-float tuple The (major, minor) axes of the SNR (unit: [ deg ]). The order of major and minor can be arbitrary. Returns ------- Tb : float Brightness temperature at the requested frequency, unit [ K ] NOTE ---- The power-law spectral shape is assumed for *flux density* other than the *brightness temperature*. Therefore, the flux density at the requested frequency should first be calculated by extrapolating the spectrum, then convert the flux density to derive the brightness temperature. """ freq_ref = self.catalog_flux_freq # [ MHz ] Fnu = flux * (frequency / freq_ref) ** (-specindex) # [ Jy ] omega = size[0] * size[1] * AUC.arcsec2deg**2 # [ arcsec^2 ] pixelarea = self.sky.pixelsize ** 2 # [ arcsec^2 ] if omega < pixelarea: # The object is smaller than a pixel, so round up to a pixel area omega = pixelarea Tb = Fnu_to_Tb_fast(Fnu, omega, frequency) return Tb def _simulate_templates(self): """ Simulate the template images/maps for each SNR, and cache these templates within this object. The template images/maps have values of (or approximate) ones for these effective pixels, excluding the pixels corresponding to the edges of original rotated ellipse, which may have values of significantly less than 1 due to the rotation. Therefore, simulating the sky map of one SNR at a requested frequency is simply multiplying these cached templates by the calculated brightness temperature (Tb) at that frequency. Furthermore, the final output sky map of all SNRs are just additions of all the maps of each SNR. Attributes ---------- templates : dict A dictionary containing the simulated templates for each SNR. The dictionary keys are the names (`self.catalog["name"]`) of the SNRs, and the values are `(idx, val)` tuples with `idx` the indexes of effective image pixels and `hpval` the values of the corresponding pixels. e.g., ``{ name1: (idx1, val1), name2: (idx2, val2), ... }`` """ templates = {} logger.info("Simulating sky template for each SNR ...") for row in self.catalog.itertuples(): name = row.name logger.debug("Simulate sky template for SNR: {0}".format(name)) gcenter = (row.glon, row.glat) # [ deg ] radii = (int(np.ceil(row.size_major * 0.5 / self.resolution)), int(np.ceil(row.size_minor * 0.5 / self.resolution))) rmax = max(radii) pcenter = (rmax, rmax) image = make_ellipse(pcenter, radii, row.rotation) wcs = make_wcs(center=gcenter, size=image.shape, pixelsize=self.resolution, frame="Galactic", projection="CAR") idx, val = self.sky.reproject_from(image, wcs, squeeze=True) templates[name] = (idx, val) logger.info("Done simulate {0} SNR templates".format(len(templates))) self.templates = templates def _simulate_single(self, data, frequency): """ Simulate one single SNR at the specified frequency. Parameters ---------- data : namedtuple The data of the SNR to be simulated, given in a ``namedtuple`` object, from which can get the required properties by ``data.key``. e.g., elements of `self.catalog.itertuples()` frequency : float The simulation frequency (unit: `self.freq_unit`). Returns ------- idx : 1D `~numpy.ndarray` The indexes of the effective map pixels for the SNR. val : 1D `~numpy.ndarray` The values (i.e., brightness temperature) of each map pixel with respect to the above indexes. See Also -------- `self._simulate_template()` for more detailed description. """ name = data.name idx, val = self.templates[name] # Calculate the brightness temperature flux = data.flux specindex = data.specindex size = (data.size_major/60.0, data.size_minor/60.0) # [ deg ] Tb = self._calc_Tb(flux, specindex, frequency, size) val = val * Tb return (idx, val) def _make_filepath(self, **kwargs): """ Make the path of output file according to the filename pattern and output directory loaded from configurations. """ data = { "prefix": self.prefix, } data.update(kwargs) filename = self.filename_pattern.format(**data) filepath = os.path.join(self.output_dir, filename) return filepath def _make_header(self): """ Make the header with detail information (e.g., parameters and history) for the simulated products. """ header = fits.Header() header["COMP"] = (self.name, "Emission component") header["BUNIT"] = ("K", "data unit is Kelvin") header["CREATOR"] = (__name__, "File creator") # TODO: history = [] comments = [] for hist in history: header.add_history(hist) for cmt in comments: header.add_comment(cmt) self.header = header logger.info("Created FITS header") def output(self, skymap, frequency): """ Write the simulated free-free map to disk with proper header keywords and history. Returns ------- outfile : str The (absolute) path to the output sky map file. """ outfile = self._make_filepath(frequency=frequency) if not hasattr(self, "header"): self._make_header() header = self.header.copy() header["FREQ"] = (frequency, "Frequency [ MHz ]") header["DATE"] = ( datetime.now(timezone.utc).astimezone().isoformat(), "File creation date" ) if self.use_float: skymap = skymap.astype(np.float32) sky = self.sky.copy() sky.data = skymap sky.header = header sky.write(outfile, clobber=self.clobber, checksum=self.checksum) return outfile def preprocess(self): """ Perform the preparation procedures for the final simulations. Attributes ---------- _preprocessed : bool This attribute presents and is ``True`` after the preparation procedures are performed, which indicates that it is ready to do the final simulations. """ if hasattr(self, "_preprocessed") and self._preprocessed: return # logger.info("{name}: preprocessing ...".format(name=self.name)) self._load_catalog() self._filter_catalog() self._add_random_rotation() # Simulate the template maps for each SNR self._simulate_templates() # self._preprocessed = True def simulate_frequency(self, frequency): """ Simulate the sky map of all Galactic SNRs emission at the specified frequency. Parameters ---------- frequency : float The simulation frequency (unit: `self.freq_unit`). Returns ------- skymap_f : 1D/2D `~numpy.ndarray` The sky map at the input frequency. filepath : str The (absolute) path to the output sky map if saved, otherwise ``None``. See Also -------- `self._simulate_template()` for more detailed description. """ self.preprocess() # logger.info("Simulating {name} map at {freq} ({unit}) ...".format( name=self.name, freq=frequency, unit=self.freq_unit)) skymap_f = np.zeros(self.sky.shape) for row in self.catalog.itertuples(): index, value = self._simulate_single(row, frequency) skymap_f[index] += value # if self.save: filepath = self.output(skymap_f, frequency) else: filepath = None return (skymap_f, filepath) def simulate(self, frequencies=None): """ Simulate the sky maps of all Galactic SNRs emission at every specified frequency. Parameters ---------- frequencies : float, or list[float] The frequencies where to simulate the foreground map. Unit: [MHz] Default: None (i.e., use ``self.frequencies``) Returns ------- skymaps : list[1D `~numpy.ndarray`] List of sky maps at each frequency. paths : list[str] List of (absolute) path to the output sky maps. """ if frequencies is not None: frequencies = np.array(frequencies, ndmin=1) else: frequencies = self.frequencies skymaps = [] paths = [] for freq in frequencies: skymap_f, outfile = self.simulate_frequency(freq) skymaps.append(skymap_f) paths.append(outfile) return (skymaps, paths) def postprocess(self): """Perform the post-simulation operations before the end.""" logger.info("{name}: postprocessing ...".format(name=self.name)) # Save the catalog actually used in the simulation self._save_catalog_inuse()