def run(n,F,A,temp,exp,psf_r,name="map",save=False): """ Brings together serveral programs to run point source Monte Carlo by reading in template, source count distribution parameters, exposure map, and the user defined PSF. :param n: numpy array of index values :param F: numpy array of flux break values :param A: float of log10 norm for the SCD :param temp: HEALPix numpy array of template :param exp: HEALPix numpy array of exposure map :param psf_r: user defined point spread function :param name: string for the name of output .npy file :param save: option to save map to .npy file :returns: HEALPix format numpy array of simulated map """ # Int. SCD to find mean couts, Poisson draws for # of sources in template num_src = iscd.run(n,F,A,temp) # Draws fluxes for each source from the SCD flux_arr = cf.run(num_src,n,F) # Generate simulated counts map map_arr = np.asarray(mm.run(num_src,flux_arr,temp,exp,psf_r)) # Save the file as an .npy file if save: np.save(str(name) + ".npy",map_arr.astype(np.int32)) print "Done simulation." return map_arr.astype(np.int32)
def run(n, F, A, temp, exp, psf_r, name="map", save=False, flux_frac=np.array([1.]), r_ROI=np.nan, returnpsdat=False): """ Brings together serveral programs to run point source Monte Carlo by reading in template, source count distribution parameters, exposure map, and the user defined PSF. :param n: numpy array of index values, highest to lowest :param F: numpy array of flux break values, highest to lowest :param A: float of log10 norm for the SCD :param temp: HEALPix numpy array of template :param exp: HEALPix numpy array of exposure map :param psf_r: user defined point spread function :param name: string for the name of output .npy file :param save: option to save map to .npy file :param flux_frac: array of flux fractions to distribute between different energy bins, default is 1 bin :params r_ROI: maximum distance to draw sources from the galactic center :params returnpsdat: if true return data on the individual sources format: [theta, phi, actual counts, expected counts, expected flux] :returns: HEALPix format numpy array of simulated map """ # If exposure map is a 1D array, wrap it again so it has 1 energy bin if len(np.shape(exp)) == 1: exp = np.array([exp]) # Check exposure map and flux frac have same number of energy bins assert(len(exp) == len(flux_frac)), \ "exposure and flux fraction must have the same number of energy bins" # Check if flux breaks in correct order if len(F) > 1: assert(F[0] > F[-1]), \ "Flux array is in the wrong order, highest to lowest!" # Int. SCD to find mean couts, Poisson draws for # of sources in template num_src = iscd.run(n, F, A, temp) # Draws fluxes for each source from the SCD flux_arr = cf.run(num_src, n, F) # Generate simulated counts map map_arr, psdat = np.asarray( mm.run(num_src, flux_arr, temp, exp, psf_r, flux_frac, r_ROI, returnpsdat)) # Save the file as an .npy file if save: np.save(str(name) + ".npy", np.array(map_arr, dtype=np.int32)) print("Done simulation.") if returnpsdat: return np.array(map_arr, dtype=np.int32), np.array(psdat, dtype=np.float) else: return np.array(map_arr, dtype=np.int32)
def heatmap(): import make_map make_map.run() return render_template('mymap.html')
def heatmap(): import make_map make_map.run() return render_template('mymap.html')