diff options
Diffstat (limited to 'fg21sim/galactic/snr.py')
| -rw-r--r-- | fg21sim/galactic/snr.py | 156 |
1 files changed, 86 insertions, 70 deletions
diff --git a/fg21sim/galactic/snr.py b/fg21sim/galactic/snr.py index a15159d..63eedf4 100644 --- a/fg21sim/galactic/snr.py +++ b/fg21sim/galactic/snr.py @@ -59,10 +59,12 @@ class SuperNovaRemnants: A Catalogue of Galactic Supernova Remnants http://www.mrao.cam.ac.uk/surveys/snrs/ """ + # Component name + name = "Galactic supernova remnants" + def __init__(self, configs): self.configs = configs self._set_configs() - self._load_catalog() def _set_configs(self): """Load the configs and set the corresponding class attributes.""" @@ -145,9 +147,6 @@ class SuperNovaRemnants: ---- The objects with uncertain data are currently kept. """ - if hasattr(self, "catalog_filtered") and self.catalog_filtered: - return - # cond1 = pd.isnull(self.catalog["size_major"]) cond2 = pd.isnull(self.catalog["size_minor"]) cond3 = pd.isnull(self.catalog["flux"]) @@ -167,16 +166,13 @@ class SuperNovaRemnants: def _add_random_rotation(self): """Add random rotation angles for each SNR as column "rotation" - within the `catalog` data frame. + 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. """ - if "rotation" in self.catalog.columns: - return - # num = len(self.catalog) angles = np.random.uniform(low=0.0, high=360.0, size=num) rotation = pd.Series(data=angles, name="rotation") @@ -270,9 +266,9 @@ class SuperNovaRemnants: Parameters ---------- data : namedtuple - The data of the SNR to be simulated, given in a `namedtuple` + The data of the SNR to be simulated, given in a ``namedtuple`` object, from which can get the required properties by - `data.key`. + ``data.key``. e.g., elements of `self.catalog.itertuples()` frequency : float The simulation frequency (unit: `self.freq_unit`). @@ -290,9 +286,6 @@ class SuperNovaRemnants: -------- `self._simulate_template()` for more detailed description. """ - if not hasattr(self, "templates"): - self._simulate_templates() - # name = data.name hpidx, hpval = self.templates[name] # Calculate the brightness temperature @@ -300,66 +293,9 @@ class SuperNovaRemnants: specindex = data.specindex size = (data.size_major, data.size_minor) Tb = self._calc_Tb(flux, specindex, frequency, size) - # Multiply the brightness temperature to the template map hpval = hpval * Tb return (hpidx, hpval) - def _simulate_frequency(self, frequency): - """Simulate the emission (HEALPix) map of all Galactic SNRs at - the specified frequency. - - Parameters - ---------- - frequency : float - The simulation frequency (unit: `self.freq_unit`). - - Returns - ------- - hpmap_f : 1D `~numpy.ndarray` - HEALPix map data in RING ordering - - See Also - -------- - `self._simulate_template()` for more detailed description. - """ - # Filter the catalog first - self._filter_catalog() - # Assign a random rotation angle for each SNR - self._add_random_rotation() - # - hpmap_f = np.zeros(hp.nside2npix(self.nside)) - for row in self.catalog.itertuples(): - hpidx, hpval = self._simulate_single(row, frequency) - hpmap_f[hpidx] += hpval - return hpmap_f - - def simulate(self, frequencies): - """Simulate the emission (HEALPix) maps of all Galactic SNRs for - every specified frequency. - - Parameters - ---------- - frequency : list[float] - List of frequencies (unit: `self.freq_unit`) where the - simulation performed. - - Returns - ------- - hpmaps : list[1D `~numpy.ndarray`] - List of HEALPix maps (in RING ordering) at each frequency. - """ - hpmaps = [] - for f in np.array(frequencies, ndmin=1): - logger.info("Simulating Galactic SNRs emission map " - "at {0} ({1}) ...".format(f, self.freq_unit)) - hpmap_f = self._simulate_frequency(f) - hpmaps.append(hpmap_f) - if self.save: - self.output(hpmap_f, f) - # Also save the catalog in use. - self._save_catalog_inuse() - return hpmaps - def _make_filepath(self, **kwargs): """Make the path of output file according to the filename pattern and output directory loaded from configurations. @@ -418,3 +354,83 @@ class SuperNovaRemnants: write_fits_healpix(filepath, hpmap, header=header, clobber=self.clobber) logger.info("Write simulated map to file: {0}".format(filepath)) + + 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 emission (HEALPix) map of all Galactic SNRs at + the specified frequency. + + Parameters + ---------- + frequency : float + The simulation frequency (unit: `self.freq_unit`). + + Returns + ------- + hpmap_f : 1D `~numpy.ndarray` + HEALPix map data in RING ordering + + 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)) + hpmap_f = np.zeros(hp.nside2npix(self.nside)) + for row in self.catalog.itertuples(): + hpidx, hpval = self._simulate_single(row, frequency) + hpmap_f[hpidx] += hpval + # + if self.save: + self.output(hpmap_f, frequency) + return hpmap_f + + def simulate(self, frequencies): + """Simulate the emission (HEALPix) maps of all Galactic SNRs for + every specified frequency. + + Parameters + ---------- + frequency : list[float] + List of frequencies (unit: `self.freq_unit`) where the + simulation performed. + + Returns + ------- + hpmaps : list[1D `~numpy.ndarray`] + List of HEALPix maps (in RING ordering) at each frequency. + """ + hpmaps = [] + for f in np.array(frequencies, ndmin=1): + hpmap_f = self.simulate_frequency(f) + hpmaps.append(hpmap_f) + return hpmaps + + 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() |