parser.add_argument("--threads", dest="threads", help="Number of threads", type=str, default=8) parser.add_argument("--logmslim", dest="logMSlim", help="Lower limit on stellar mass", type=str, default=None) args = parser.parse_args() ncore = int(args.threads) logSMlim = float(args.logMSlim) # Initiate my likelihood model model = Likelihood.Model(logSMlim, generator=True) print("Initiated the model") sys.stdout.flush() # How dense should the grid be alphas = np.linspace(p.min_alpha, p.max_alpha, p.Nalphas) scatters = np.linspace(p.min_scatter, p.max_scatter, p.Nscatters) # Say x-dimension corresponds to alpha, y-dimension corresponds to scatter XX, YY = np.meshgrid(alphas, scatters) ndim1, ndim2 = XX.shape # Calculate the stochastic covariance matrix at these values Niter = 40 Ntot = XX.size
type=str, default=8) parser.add_argument("--perccat", dest="perccat", help="Sets how much of the catalog to exclude", type=str, default=None) args = parser.parse_args() ncores = int(args.threads) perccat = float(args.perccat) cuts_def = p.load_pickle("../../Data/BMmatching/logMBcuts_def.p") logBMlim = cuts_def[perccat] # Initiate my likelihood model model = Likelihood.Model(logBMlim, perccat, generator=True) print("Initiated the model!") sys.stdout.flush() alphas = np.linspace(p.min_alpha, p.max_alpha, p.Nalphas) scatters = np.linspace(p.min_scatter, p.max_scatter, p.Nscatters) # Say x-dimension corresponds to alpha, y-dimension corresponds to scatter XX, YY = np.meshgrid(alphas, scatters) ndim1, ndim2 = XX.shape # Calculate the stochastic covariance matrix at these values Niter = 1 covmats = np.zeros(shape=(ndim1, ndim2, p.nbins, p.nbins)) Ntot = XX.size k = 1 extime = list()