def set_fn_model(flg=0): ''' Load up f(N) data Parameters ---------- Returns ------- fN_data :: List of fN_Constraint Classes JXP on 27 Nov 2014 ''' if flg == 0: # I may choose to pickle a few of these sfN_model = xifm.default_model(recalc=True, use_mcmc=True) # Hermite Spline elif flg == 1: sfN_model = xifm.fN_Model('Gamma') else: raise ValueError( 'mcmc.set_model: Not ready for this type of fN model {:d}'.format( flg)) # #print(sfN_model) return sfN_model
def set_fn_model(flg=0): ''' Load up f(N) data Parameters ---------- Returns ------- fN_data :: List of fN_Constraint Classes JXP on 27 Nov 2014 ''' if flg==0: # I may choose to pickle a few of these sfN_model = xifm.default_model(recalc=True,use_mcmc=True) # Hermite Spline elif flg==1: sfN_model = xifm.fN_Model('Gamma') else: raise ValueError('mcmc.set_model: Not ready for this type of fN model {:d}'.format(flg)) # #print(sfN_model) return sfN_model
#flg_test += 2**3 # l(X) #flg_test += 64 # Akio #flg_test += 2**7 # rho_HI flg_test += 2**8 # Create Pickle file #flg_test = 0 + 64 if (flg_test % 2) == 1: # MCMC Analysis chain_file = os.environ.get( 'DROPBOX_DIR') + 'IGM/fN/MCMC/mcmc_spline_k13r13o13n12_8.fits.gz' outp = mcmc.chain_stats(chain_file) # Build a model NHI_pivots = [12., 15., 17.0, 18.0, 20.0, 21., 21.5, 22.] fN_model = xifm.fN_Model('Hspline', zmnx=(0.5, 3.0), pivots=NHI_pivots, param=outp['best_p']) #xdb.set_trace() print(fN_model) # Compare default against P+13 if (flg_test % 2**2) >= 2**1: fN_model = xifm.default_model() p13_file = (os.environ.get('DROPBOX_DIR') + 'IGM/fN/fN_spline_z24.fits.gz') hdu = fits.open(p13_file) p13_data = hdu[1].data plt.clf() plt.scatter(p13_data['LGN'], p13_data['FN']) #plt.plot(p13_data['LGN'],p13_data['FN'], '-')
flg_test = 0 #flg_test += 2**1 # Compare models #flg_test += 2**2 # Data #flg_test += 2**3 # l(X) #flg_test += 64 # Akio flg_test += 2**7 # rho_HI #flg_test = 0 + 64 if (flg_test % 2) == 1: # MCMC Analysis chain_file = os.environ.get('DROPBOX_DIR')+'IGM/fN/MCMC/mcmc_spline_k13r13o13n12_8.fits.gz' outp = mcmc.chain_stats(chain_file) # Build a model NHI_pivots = [12., 15., 17.0, 18.0, 20.0, 21., 21.5, 22.] fN_model = xifm.fN_Model('Hspline', zmnx=(0.5,3.0), pivots=NHI_pivots, param=outp['best_p']) #xdb.set_trace() print(fN_model) # Compare default against P+13 if (flg_test % 4) >= 2: fN_model = xifm.default_model() p13_file = (os.environ.get('DROPBOX_DIR')+'IGM/fN/fN_spline_z24.fits.gz') hdu = fits.open(p13_file) p13_data = hdu[1].data plt.clf() plt.scatter(p13_data['LGN'],p13_data['FN']) #plt.plot(p13_data['LGN'],p13_data['FN'], '-') xplt = np.linspace(12., 22, 10000) yplt = fN_model.eval(xplt, 2.4)