From 5d8729cb1d835212e1b848d48339acb2e05eb278 Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Fri, 8 Nov 2024 18:52:30 +0100 Subject: [PATCH 1/8] many changes and adaptations for SKA GC and IM --- cosmicfishpie/LSSsurvey/spectro_cov.py | 173 ++++++-------- cosmicfishpie/LSSsurvey/spectro_obs.py | 287 ++++++++++------------- cosmicfishpie/configs/config.py | 166 +++++++------ cosmicfishpie/cosmology/nuisance.py | 92 +++++--- cosmicfishpie/fishermatrix/cosmicfish.py | 71 +++--- 5 files changed, 384 insertions(+), 405 deletions(-) diff --git a/cosmicfishpie/LSSsurvey/spectro_cov.py b/cosmicfishpie/LSSsurvey/spectro_cov.py index d45a98d..8017c46 100644 --- a/cosmicfishpie/LSSsurvey/spectro_cov.py +++ b/cosmicfishpie/LSSsurvey/spectro_cov.py @@ -18,7 +18,8 @@ class SpectroCov: def __init__( - self, fiducialpars, fiducial_specobs=None, bias_samples=["g", "g"], configuration=None + self, fiducialpars, fiducial_specobs=None, bias_samples=["g", "g"], + configuration=None ): """ Initializes an object with specified fiducial parameters and computes @@ -41,8 +42,6 @@ def __init__( ---------- pk_obs : cosmicfishpie.LSSsurvey.spectro_obs.ComputeGalSpectro, cosmicfishpie.LSSsurvey.spectro_obs.ComputeGalIM Fiducial instance of the observable of the spectroscopic probe. Either Galaxy Clustering, Intensity mapping or cross correlation. - pk_obs_gg : cosmicfishpie.LSSsurvey.spectro_obs.ComputeGalSpectro, cosmicfishpie.LSSsurvey.spectro_obs.ComputeGalIM - Fiducial instance of the galaxy clustering autocorrelation observable of the spectroscopic probe if cross correlation is asked for. pk_obs_II : cosmicfishpie.LSSsurvey.spectro_obs.ComputeGalSpectro, cosmicfishpie.LSSsurvey.spectro_obs.ComputeGalIM Fiducial instance of the intensity mapping autocorrelation observable of the spectroscopic probe if cross correlation is asked for. area_survey_spectro : float @@ -63,13 +62,10 @@ def __init__( Redshift bin edges """ # initializing the class only with fiducial parameters - # if fiducial_specobs is None: - if configuration is None: self.config = cfg else: self.config = configuration - self.feed_lvl = self.config.settings["feedback"] try: self.fsky_spectro = self.config.specs["fsky_spectro"] @@ -77,45 +73,14 @@ def __init__( except KeyError: self.area_survey = self.config.specs["area_survey_spectro"] self.fsky_spectro = self.area_survey / upm.areasky() - if "IM" in self.config.obs and "GCsp" in self.config.obs: - bias_samples = ["I", "g"] - upt.time_print( - feedback_level=self.feed_lvl, - min_level=2, - text="Entering Cov cross XC IM,g term", - instance=self, - ) - self.pk_obs = spec_obs.ComputeGalIM( - fiducialpars, fiducialpars, bias_samples=bias_samples, configuration=self.config - ) - self.pk_obs_gg = spec_obs.ComputeGalIM( - fiducialpars, fiducialpars, bias_samples=["g", "g"], configuration=self.config - ) - self.pk_obs_II = spec_obs.ComputeGalIM( - fiducialpars, fiducialpars, bias_samples=["I", "I"], configuration=self.config - ) - elif "IM" in self.config.obs and "I" in bias_samples: - bias_samples = ["I", "I"] - upt.time_print( - feedback_level=self.feed_lvl, - min_level=2, - text="Entering Cov IM term", - instance=self, - ) - self.pk_obs = spec_obs.ComputeGalIM( - fiducialpars, fiducialpars, bias_samples=bias_samples, configuration=self.config - ) - elif "GCsp" in self.config.obs and "g" in bias_samples: - bias_samples = ["g", "g"] - upt.time_print( - feedback_level=self.feed_lvl, - min_level=2, - text="Entering Cov gg term", - instance=self, - ) + if fiducial_specobs is None: self.pk_obs = spec_obs.ComputeGalSpectro( - fiducialpars, fiducialpars, bias_samples=bias_samples, configuration=self.config - ) + fiducialpars, + fiducial_cosmopars=fiducialpars, + bias_samples=bias_samples, + configuration=self.config + ) + # if no other parameters are provided, the method will use the fiducials from config else: self.pk_obs = fiducial_specobs @@ -126,20 +91,23 @@ def __init__( self.dz_bins = np.diff(self.z_bins) self.global_z_bin_mids = self.z_bin_mids self.global_z_bins = self.z_bins + self.inter_z_bin_mids = self.z_bin_mids + self.inter_z_bins = self.z_bins if "IM" in self.pk_obs.observables: - self.IM_z_bins = self.pk_obs.nuisance.IM_zbins() - self.IM_z_bin_mids = self.pk_obs.nuisance.IM_zbins_mids() + self.IM_z_bins = self.pk_obs.nuisance.IM_zbins + self.IM_z_bin_mids = self.pk_obs.nuisance.IM_zbins_mids self.Tsys_interp = self.pk_obs.nuisance.IM_THI_noise() self.global_z_bin_mids = self.IM_z_bin_mids self.global_z_bins = self.IM_z_bins + self.inter_z_bin_mids = self.IM_z_bin_mids + self.inter_z_bins = self.IM_z_bins # Choose longest zbins array to loop in Fisher matrix if "GCsp" in self.pk_obs.observables and "IM" in self.pk_obs.observables: - if len(self.z_bin_mids) >= len(self.IM_z_bin_mids): - self.global_z_bin_mids = self.z_bin_mids - self.global_z_bins = self.z_bins - else: - self.global_z_bin_mids = self.IM_z_bin_mids - self.global_z_bins = self.IM_z_bins + self.global_z_bin_mids = np.union1d(self.z_bin_mids, self.IM_z_bin_mids) + self.global_z_bins = np.union1d(self.z_bins, self.IM_z_bins) + ## overlapping z bins + self.inter_z_bin_mids = np.intersect1d(self.z_bin_mids, self.IM_z_bin_mids) + self.inter_z_bins = np.intersect1d(self.z_bins, self.IM_z_bins) def Tsys_func(self, z): """Calculates Tsys in mK @@ -209,7 +177,7 @@ def volume_survey(self, ibin): vol = self.fsky_spectro * self.d_volume(ibin) return vol - def n_density(self, ibin): + def n_density(self, zi): """calculate the comoving number density of the probe Parameters @@ -222,17 +190,22 @@ def n_density(self, ibin): float comoving number density of the probe """ + try: + ibin = np.where(np.isclose(self.inter_z_bin_mids, zi, rtol=1e-2))[0][0] + except IndexError: + print(f"Warning: zi = {zi} not in global_z_bin_mids") + return 0 ndens = self.dnz[ibin] * self.dz_bins[ibin] / self.d_volume(ibin) ndens = upm.areasky() * ndens ## multiply with the full sky area in degrees return ndens - def veff(self, ibin, k, mu): + def veff(self, zi, k, mu): """calculate the effective volume entering the covariance of the galaxy clustering probe Parameters ---------- - i : int - Index of the survey redshift bin + zi : float + Redshift of the inner sphere k : float, numpy.ndarray wave number at which the effective volume should be calculated mu : float, numpy.ndarray @@ -242,10 +215,11 @@ def veff(self, ibin, k, mu): float, numpy.ndarray The effective volume for a given wavenumber, angle and redshift """ - zi = self.z_bin_mids[ibin] - npobs = self.n_density(ibin) * self.pk_obs.observed_Pgg(zi, k, mu) + npobs = self.n_density(zi) * self.pk_obs.observed_Pgg(zi, k, mu) prefactor = 1 / (8 * (np.pi**2)) covterm = prefactor * (npobs / (1 + npobs)) ** 2 + if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: + covterm = np.zeros_like(covterm) return covterm def cov(self, ibin, k, mu): @@ -265,7 +239,7 @@ def cov(self, ibin, k, mu): float, numpy.ndarray Covariance of the Galaxy clustering probe """ - zmid = self.z_bin_mids[ibin] + zmid = self.global_z_bin_mids[ibin] veffS = self.veff(ibin, k, mu) * self.volume_survey(ibin) pobs = self.pk_obs.observed_Pgg(zmid, k, mu) prefactor = 2 * (2 * np.pi) ** 3 @@ -298,7 +272,7 @@ def P_noise_21(self, z, k, mu, temp_dim=True, beam_term=False): elif temp_dim: temp = 1 pref = (2 * np.pi * self.pk_obs.fsky_IM) / (self.pk_obs.f_21 * self.pk_obs.t_tot) - cosmo = ((1 + z) ** 2 * self.pk_obs.cosmo.comoving(z) ** 2) / self.pk_obs.cosmo.Hubble(z) + cosmo = ((1 + z) ** 2 * self.pk_obs.fiducialcosmo.comoving(z) ** 2) / self.pk_obs.fiducialcosmo.Hubble(z) T_term = (self.Tsys_func(z) / temp) ** 2 # in K alpha = self.pk_obs.alpha_SD() if beam_term: @@ -308,13 +282,13 @@ def P_noise_21(self, z, k, mu, temp_dim=True, beam_term=False): noise = pref * cosmo * T_term * (alpha / beta**2) return noise - def veff_21cm(self, ibin, k, mu): + def veff_II(self, zi, k, mu): """calculate the effective volume entering the covariance of the line intensity mapping probe Parameters ---------- - i : int - Index of the survey redshift bin + zi : float + Redshift k : float, numpy.ndarray wave number at which the effective volume should be calculated mu : float, numpy.ndarray @@ -324,20 +298,21 @@ def veff_21cm(self, ibin, k, mu): float, numpy.ndarray The effective volume for a given wavenumber, angle and redshift """ - zi = self.IM_z_bin_mids[ibin] - pobs = self.pk_obs.observed_P_HI(zi, k, mu) - pnoisy = pobs + self.P_noise_21(zi, k, mu) + pobs = self.pk_obs.observed_P_ij(zi, k, mu, si="I", sj="I") + pnoisy = self.noisy_P_ij(zi, k, mu, si="I", sj="I") prefactor = 1 / (8 * (np.pi**2)) covterm = prefactor * (pobs / pnoisy) ** 2 + if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: + covterm = np.zeros_like(covterm) return covterm - def veff_XC(self, ibin, k, mu): + def veff_Ig(self, zi, k, mu): """calculate the effective volume entering the covariance of the cross correlation of galaxy clustering and intensity mapping Parameters ---------- - i : int - Index of the survey redshift bin + zi : float + Redshift k : float, numpy.ndarray wave number at which the effective volume should be calculated mu : float, numpy.ndarray @@ -348,19 +323,29 @@ def veff_XC(self, ibin, k, mu): The effective volume for a given wavenumber, angle and redshift """ print("Entering veff_XC term") - zi = self.IM_z_bin_mids[ibin] # when calling this function, this is the XC spectrum - pobs_Ig = self.pk_obs.observed_P_HI(zi, k, mu) - pobs_gg = self.pk_obs_gg.observed_P_HI(zi, k, mu) - pobs_II = self.pk_obs_II.observed_P_HI(zi, k, mu) - pnoisy_Ig = pobs_Ig - pnoisy_II = pobs_II + self.P_noise_21(zi, k, mu) - pnoisy_gg = pobs_gg + self.n_density(ibin) + # the si, sj indices will be overwritten by the self.bias_samples in the observed_P_Ig function + pobs_Ig = self.pk_obs.observed_P_ij(zi, k, mu, si="I", sj="g") + pnoisy_Ig = self.noisy_P_ij(zi, k, mu, si="I", sj="g") + pnoisy_II = self.noisy_P_ij(zi, k, mu, si="I", sj="I") + pnoisy_gg = self.noisy_P_ij(zi, k, mu, si="g", sj="g") covterm = pobs_Ig**2 / (pnoisy_gg * pnoisy_II + pnoisy_Ig * pnoisy_Ig) prefactor = 1 / (4 * (np.pi**2)) covterm = prefactor * covterm + if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: + covterm = np.zeros_like(covterm) return covterm - + + def noisy_P_ij(self, z, k, mu, si="I", sj="g"): + if si == "I" and sj == "I": + noiseterm = self.P_noise_21(z, k, mu, temp_dim=True) + elif si == "g" and sj == "g": + noiseterm = 1/self.n_density(z) + else: + noiseterm = 0 + pobs_ij = self.pk_obs.observed_P_ij(z, k, mu, si=si, sj=sj) + pnoisy_ij = pobs_ij + noiseterm + return pnoisy_ij class SpectroDerivs: def __init__( @@ -432,6 +417,8 @@ def __init__( self.fiducial_spectrobiaspars = fiducial_spectro_obj.fiducial_spectrobiaspars if "IM" in self.observables: self.fiducial_IMbiaspars = fiducial_spectro_obj.fiducial_IMbiaspars + else: + self.fiducial_IMbiaspars = None self.fiducial_PShotpars = fiducial_spectro_obj.PShotpars self.fiducial_allpars = fiducial_spectro_obj.fiducial_allpars self.fiducial_spectrononlinearpars = fiducial_spectro_obj.fiducial_spectrononlinearpars @@ -448,27 +435,17 @@ def initialize_obs(self, allpars): spectrobiaspars = dict((k, allpars[k]) for k in self.fiducial_spectrobiaspars) PShotpars = dict((k, allpars[k]) for k in self.fiducial_PShotpars) spectrononlinearpars = dict((k, allpars[k]) for k in self.fiducial_spectrononlinearpars) - - if "I" in self.bias_samples: + if "IM" in self.observables: IMbiaspars = dict((k, allpars[k]) for k in self.fiducial_IMbiaspars) - - self.pobs = spec_obs.ComputeGalIM( - cosmopars=cosmopars, - fiducial_cosmopars=self.fiducial_cosmopars, - spectrobiaspars=spectrobiaspars, - IMbiaspars=IMbiaspars, - PShotpars=PShotpars, - fiducial_cosmo=self.fiducial_cosmo, - bias_samples=self.bias_samples, - configuration=self.config, - ) - elif "g" in self.bias_samples: - self.pobs = spec_obs.ComputeGalSpectro( + else: + IMbiaspars = None + self.pobs = spec_obs.ComputeGalSpectro( cosmopars=cosmopars, fiducial_cosmopars=self.fiducial_cosmopars, spectrobiaspars=spectrobiaspars, spectrononlinearpars=spectrononlinearpars, PShotpars=PShotpars, + IMbiaspars=IMbiaspars, fiducial_cosmo=self.fiducial_cosmo, bias_samples=self.bias_samples, configuration=self.config, @@ -495,10 +472,12 @@ def get_obs(self, allpars): result_array = dict() result_array["z_bins"] = self.z_array for ii, zzi in enumerate(self.z_array): - if "I" in self.bias_samples: - result_array[ii] = self.pobs.lnpobs_IM(zzi, self.pk_kmesh, self.pk_mumesh) - elif "g" in self.bias_samples: + if self.bias_samples == ["I", "I"]: + result_array[ii] = self.pobs.lnpobs_ij(zzi, self.pk_kmesh, self.pk_mumesh, si="I", sj="I") + elif self.bias_samples == ["g", "g"]: result_array[ii] = self.pobs.lnpobs_gg(zzi, self.pk_kmesh, self.pk_mumesh) + elif self.bias_samples == ["I", "g"] or self.bias_samples == ["g", "I"]: + result_array[ii] = self.pobs.lnpobs_ij(zzi, self.pk_kmesh, self.pk_mumesh, si="I", sj="g") return result_array def exact_derivs(self, par): @@ -518,7 +497,7 @@ def exact_derivs(self, par): deriv = dict() for ii, zzi in enumerate(self.z_array): pgg_obs = self.pobs.observed_Pgg(zzi, self.pk_kmesh, self.pk_mumesh) - z_bin_mids = self.pobs.gcsp_z_bin_mids + z_bin_mids = self.z_array kron = self.kronecker_bins(par, z_bin_mids, zzi) deriv_i = 1 / pgg_obs deriv[ii] = kron * deriv_i @@ -695,7 +674,7 @@ def dlnpobs_dnuisp(self, zi, k, mu, par): print("Error: Param name not supported in nuisance parameters") deriv = 0 # get index in bin - ii = np.where(np.isclose(self.z_bin_mids, zi)) + ii = np.where(np.isclose(self.global_z_bin_mids, zi)) ii = ii[0][0] + 1 pi = par.split("_") pi = int(pi[-1]) diff --git a/cosmicfishpie/LSSsurvey/spectro_obs.py b/cosmicfishpie/LSSsurvey/spectro_obs.py index 94ae654..f4f6dc3 100644 --- a/cosmicfishpie/LSSsurvey/spectro_obs.py +++ b/cosmicfishpie/LSSsurvey/spectro_obs.py @@ -25,96 +25,97 @@ def __init__( cosmopars, fiducial_cosmopars=None, spectrobiaspars=None, + IMbiaspars=None, spectrononlinearpars=None, PShotpars=None, fiducial_cosmo=None, - bias_samples=["g", "g"], + bias_samples=None, use_bias_funcs=False, configuration=None, ): - """class to compute the observed power spectrum of a spectroscopic galaxy clustering experiment + """Class to compute the observed power spectrum for spectroscopic galaxy clustering and intensity mapping experiments. Parameters ---------- cosmopars : dict - A dictionary containing the cosmological parameters of the sample cosmology + A dictionary containing the cosmological parameters of the sample cosmology fiducial_cosmopars : dict, optional - A dictionary containing the cosmological parameters of the fiducial/reference cosmology - spectro_biaspars : dict, optional - A dictionary containing the specifications for the galaxy biases + A dictionary containing the cosmological parameters of the fiducial/reference cosmology + spectrobiaspars : dict, optional + A dictionary containing the specifications for the galaxy biases + IMbiaspars : dict, optional + A dictionary containing the specifications for the intensity mapping biases spectrononlinearpars : dict, optional - A dictionary containing the values of the non linear modeling parameters entering FOG and the dewiggling weight per bin - PShotpar : dict, optional - A dictionary containing the values of the additional shot noise per bin - fiducial_cosmo : cosmicfishpie.cosmology.cosmology.cosmo_functions, optional - An instance of `cosmo_functions` of the fiducial cosmology. - bias_samples : list - A list of two strings specifying if galaxy clustering, intensity mapping or cross correlation power spectrum should be computed. Use "g" for galaxy and "I" for intensity mapping. (default ['g', 'g']) - use_bias_func : bool - If True will compute the bias function by constructing it from the specification file. If False it will be recomputed from spectro_biaspars + A dictionary containing the values of the non linear modeling parameters entering FOG and the dewiggling weight per bin + PShotpars : dict, optional + A dictionary containing the values of the additional shot noise per bin + fiducial_cosmo : cosmicfishpie.cosmology.cosmology.cosmo_functions, optional + An instance of `cosmo_functions` of the fiducial cosmology + bias_samples : list, optional + A list of two strings specifying if galaxy clustering, intensity mapping or cross correlation power spectrum should be computed. + Use "g" for galaxy and "I" for intensity mapping. Default: ["g", "g"] + use_bias_funcs : bool, optional + If True will compute the bias function by constructing it from the specification file. + If False it will be recomputed from bias parameters. Default: False + configuration : object, optional + Configuration object containing settings and parameters. If None, uses default config Attributes ---------- feed_lvl : int - Number indicating the verbosity of the output. Higher numbers mean more output + Number indicating the verbosity of the output. Higher numbers mean more output observables : list - A list of the observables that the observed power spectrum is computed for + A list of the observables that the observed power spectrum is computed for s8terms : bool - If True will expand the observed power spectrum with :math:`\\sigma_8` to match the IST:F recipe + If True will expand the observed power spectrum with :math:`\\sigma_8` to match the IST:F recipe tracer : str - What Power spectrum should be used when calculating the angular power spectrum of galaxy clustering. Either "matter" or "clustering" - fiducial_cosmopars : dict - A dictionary containing the cosmological parameters of the fiducial/reference cosmology - fiducial_cosmo : cosmicfishpie.cosmology.cosmology.cosmo_functions - An instance of `cosmo_functions` of the fiducial cosmology. + What Power spectrum should be used when calculating the power spectrum. Either "matter" or "clustering" cosmo : cosmicfishpie.cosmology.cosmology.cosmo_functions - An instance of `cosmo_functions` of the sample cosmology. + An instance of `cosmo_functions` of the sample cosmology nuisance : cosmicfishpie.cosmology.Nuisance.Nuisance - An instance of `nuisance` that contains the relevant modeling of nuisance parameters - gcsp_bias_of_z : callable - Function that when passed a numpy.ndarray of redshifts will return the spectroscopic galaxy bias + An instance of `nuisance` that contains the relevant modeling of nuisance parameters extraPshot : dict - A dictionary containing the values of the additional shot noise per bin - bias_samples : list - A list of two strings specifying if galaxy clustering, intensity mapping or cross correlation power spectrum should be computed. Use "g" for galaxy and "I" for intensity mapping. - gcsp_z_bin_mids : numpy.ndarray - Lists the redshift bin centers - fiducial_spectrobiaspars : dict - A dictionary containing the fiducial values for the galaxy biases - use_bias_funcs : bool - If True will compute the bias function by constructing it from the specification file. If False it will be recomputed from spectro_biaspars - spectrobiaspars : dict - A dictionary containing the specifications for the galaxy biases - fiducial_PShotpars : dict - A dictionary containing the fiducial values of the additional shot noise per bin - PShotpars : dict - A dictionary containing the values of the additional shot noise per bin - fiducial_spectrononlinearpars : dict - A dictionary containing the fiducial values of the non linear modeling parameters entering FOG and the dewiggling weight per bin - spectrononlinearpars : dict - A dictionary containing the values of the non linear modeling parameters entering FOG and the dewiggling weight per bin - sigmap_inter : callable - A callable function that when given a numpy.ndarray of redshifts will give the interpolated value of the non linear modeling parameters entering FOG - sigmav_inter : callable - A callable function that when given a numpy.ndarray of redshifts will give the interpolated value of the non linear modeling parameters entering the dewiggling weight - allpars : dict - Dictionary containing all relevant parameters to compute the observed power spectrum - fiducial_allpars : dict - Dictionary containing all relevant fiducial parameters to compute the observed power spectrum + A dictionary containing the values of the additional shot noise per bin + gcsp_z_bin_mids : numpy.ndarray + Lists the redshift bin centers for galaxy clustering + IM_z_bin_mids : numpy.ndarray + Lists the redshift bin centers for intensity mapping k_grid : numpy.ndarray - Lists all wavenumbers used in the internal calculations + Lists all wavenumbers used in the internal calculations dk_grid : numpy.ndarray - Lists the numerical distance between all wavenumbers used in the internal calculations + Lists the numerical distance between all wavenumbers used in the internal calculations linear_switch : bool - If False all nonlinear effects will neglected in the computation of the observed power spectrum + If False all nonlinear effects will be included in the computation FoG_switch : bool - If True and `linear_switch` is True, then the finger of god effect will be modelled in the observed power spectrum. + If True and `linear_switch` is True, then the finger of god effect will be modelled APbool : bool - If True and `linear_switch` is True, then the Alcock-Paczynsk effect be considered + If True and `linear_switch` is True, then the Alcock-Paczynski effect will be considered fix_cosmo_nl_terms : bool - If True and the nonlinear modeling parameters are not varied, then they will be fixed to the values computed in the fiducial cosmology. Else they will be recomputed in each sample cosmology + If True and the nonlinear modeling parameters are not varied, then they will be fixed to the fiducial cosmology values dz_err : float - Value of the spectroscopic redshift error + Value of the spectroscopic redshift error + Dd : float + Dish diameter for intensity mapping in meters + lambda_21 : float + 21cm wavelength in meters + fsky_IM : float + Sky fraction for intensity mapping + t_tot : float + Total observation time in seconds + N_d : int + Number of dishes for intensity mapping + + Notes + ----- + This class can compute: + - Galaxy clustering power spectrum + - HI intensity mapping power spectrum + - Cross-correlation between galaxy clustering and intensity mapping + + The type of power spectrum is determined by the `bias_samples` parameter: + - ["g", "g"]: Galaxy auto-correlation + - ["I", "I"]: Intensity mapping auto-correlation + - ["g", "I"] or ["I", "g"]: Cross-correlation """ tini = time() if configuration is None: @@ -160,10 +161,17 @@ def __init__( spectrononlinearpars = self.fiducial_spectrononlinearpars self.spectrononlinearpars = spectrononlinearpars + self.fiducial_IMbiaspars = deepcopy(self.config.IMbiasparams) + if IMbiaspars is None: + self.IMbiaspars = self.fiducial_IMbiaspars + else: + self.IMbiaspars = IMbiaspars + self.nuisance = nuisance.Nuisance( configuration=self.config, spectrobiasparams=self.spectrobiaspars, spectrononlinearpars=self.spectrononlinearpars, + IMbiasparams=self.IMbiaspars, ) self.extraPshot = self.nuisance.extra_Pshot_noise() self.gcsp_z_bin_mids = self.nuisance.gcsp_zbins_mids() @@ -176,22 +184,34 @@ def __init__( self.allpars = { **self.cosmopars, **self.spectrobiaspars, + **self.IMbiaspars, **self.PShotpars, **self.spectrononlinearpars, } self.fiducial_allpars = { **self.fiducial_cosmopars, **self.fiducial_spectrobiaspars, + **self.fiducial_IMbiaspars, **self.fiducial_PShotpars, **self.fiducial_spectrononlinearpars, } + if "IM" in self.observables and "GCsp" in self.observables: + self.obs_spectrum = ["I", "g"] + elif "IM" in self.observables: # compute in the case of IM only + self.obs_spectrum = ["I", "I"] + elif "GCsp" in self.observables: + self.obs_spectrum = ["g", "g"] self.set_internal_kgrid() self.activate_terms() self.set_spectro_dz_specs() self.bias_samples = bias_samples self.use_bias_funcs = use_bias_funcs self.set_spectro_bias_specs() + if "IM" in self.observables: + self.set_IM_specs() + self.set_IM_bias_specs() + tend = time() upt.time_print( feedback_level=self.feed_lvl, @@ -497,21 +517,29 @@ def bterm_fid(self, z, k=None, bias_sample="g"): bias_sample : str, optional Specifies whether to compute the galaxy ('g') or intensity mapping ('I') bias term. (default='g') - Returns: + Returns -------- float The value of the bias term at `z` and `k`, if provided. """ - if bias_sample != self.sp_bias_sample: - raise ValueError( - f"Bias sample {bias_sample} not found. " - f"Please use {self.sp_bias_sample} bias sample." - ) - if self.use_bias_funcs: - bfunc_of_z = self.nuisance.gcsp_bias_interp() - bterm_z = bfunc_of_z(z) - else: - bterm_z = self.nuisance.vectorized_gcsp_bias_at_z(z) + if bias_sample == "g": + if bias_sample != self.sp_bias_sample: + raise ValueError( + f"Bias sample {bias_sample} not found. " + f"Please use {self.sp_bias_sample} bias sample." + ) + if self.use_bias_funcs: + bfunc_of_z = self.nuisance.gcsp_bias_interp() + bterm_z = bfunc_of_z(z) + else: + bterm_z = self.nuisance.vectorized_gcsp_bias_at_z(z) + elif bias_sample == "I": + if bias_sample != self.IM_bias_sample: + raise ValueError( + f"Bias sample {bias_sample} not found. " + f"Please use {self.IM_bias_sample} bias sample." + ) + bterm_z = self.nuisance.IM_bias_at_z(z) bterm_k = self.nuisance.gcsp_bias_kscale(k) bterm_zk = bterm_z * bterm_k return bterm_zk @@ -867,74 +895,13 @@ def lnpobs_gg(self, z, k, mu, b_i=None): pobs = self.observed_Pgg(z, k, mu, b_i=b_i) return np.log(pobs) - -class ComputeGalIM(ComputeGalSpectro): - def __init__( - self, - cosmopars, - fiducial_cosmopars=None, - spectrobiaspars=None, - IMbiaspars=None, - PShotpars=None, - fiducial_cosmo=None, - use_bias_funcs=True, - bias_samples=["I", "I"], - configuration=None, - ): - super().__init__( - cosmopars, - fiducial_cosmopars=fiducial_cosmopars, - spectrobiaspars=spectrobiaspars, - PShotpars=PShotpars, - fiducial_cosmo=fiducial_cosmo, - use_bias_funcs=True, - bias_samples=bias_samples, - configuration=configuration, - ) - - tini = time() - self.feed_lvl = self.config.settings["feedback"] - upt.time_print( - feedback_level=self.feed_lvl, min_level=2, text="Entered ComputeGalIM", instance=self - ) - - if "IM" not in self.observables: - raise AttributeError("Observables list not defined properly") - self.fiducial_IMbiaspars = self.config.IMbiasparams - self.use_bias_funcs = use_bias_funcs - if IMbiaspars is None: - IMbiaspars = self.fiducial_IMbiaspars - else: - # If IMbiaspars are not passed explicitly, use interpolated bias - # funcs - self.use_bias_funcs = False - self.IMbiaspars = IMbiaspars - self.set_IM_specs() - self.IM_bias_of_z = self.nuisance.IM_bias - self.IM_z_bin_mids = self.nuisance.IM_zbins_mids() - print("Bias samples", self.bias_samples) - self.allpars = { - **self.cosmopars, - **self.spectrobiaspars, - **self.IMbiaspars, - **self.PShotpars, - } - self.fiducial_allpars = { - **self.fiducial_cosmopars, - **self.fiducial_spectrobiaspars, - **self.fiducial_IMbiaspars, - **self.fiducial_PShotpars, - } - - tend = time() - upt.time_print( - feedback_level=self.feed_lvl, - min_level=2, - text="GalIM initialization done in: ", - time_ini=tini, - time_fin=tend, - instance=self, - ) + def set_IM_bias_specs(self): + """Updates the IM bias choices""" + self.IM_bias_model = self.specs["IM_bias_model"] + self.IM_bias_root = self.specs["IM_bias_root"] + self.IM_bias_sample = self.specs["IM_bias_sample"] + self.IM_bias_of_z = self.nuisance.IM_bias_at_z + self.IM_z_bin_mids = self.nuisance.IM_zbins_mids def set_IM_specs(self): self.Dd = self.config.specs["D_dish"] # Dish diameter in m @@ -946,22 +913,19 @@ def set_IM_specs(self): # HZ, for MHz: MHz /1e6 self.f_21 = (self.cosmo.c * 1000) / self.lambda_21 - # def IM_bias(self, z): - # """ - # b(z) for HI 21cm IM from nuisance module - # """ - # bb = self.nuisance.IM_bias(z) - # return bb - def Omega_HI(self, z): o = 4 * np.power((1 + z), 0.6) * 1e-4 return o - def Temperature(self, z): + def Temperature(self, z, fixed_cosmo=True): """obtaining the temperature (T^2(z)) for the Power Spectrum (PHI(z))""" - h = self.cosmopars["h"] - H0 = self.cosmo.Hubble(0.0) - temp = 189 * h * (1 + z) ** 2 * (H0 / self.cosmo.Hubble(z)) * self.Omega_HI(z) + if fixed_cosmo: + cocosmo = self.fiducialcosmo + else: + cocosmo = self.cosmo + h = cocosmo.cosmopars["h"] + H0 = cocosmo.Hubble(0.0) + temp = 189 * h * (1 + z) ** 2 * (H0 / cocosmo.Hubble(z)) * self.Omega_HI(z) # temperature in mK return temp @@ -981,23 +945,22 @@ def beta_SD(self, z, k, mu): bet[np.abs(bet) < tol] = tol return bet - def observed_P_HI(self, z, k, mu, bsi_z=None, bsj_z=None, si="I", sj="I"): - k = self.k_units_change(k) # has to be done before spec_err and AP - error_z = self.spec_err_z(z, k, mu) # before rescaling of k,mu by AP + def observed_P_ij(self, z, k, mu, bsi_z=None, bsj_z=None, si="I", sj="g"): + error_z = self.spec_err_z(z, k, mu) + beam_damping_term_si = self.beta_SD(z, k, mu) if si == "I" else 1 + beam_damping_term_sj = self.beta_SD(z, k, mu) if sj == "I" else 1 + k = self.k_units_change(k) # h-bug set to False by default, leaving here for cross-check of old cases k, mu = self.kmu_alc_pac(z, k, mu) - if self.bias_samples is not None: - si = self.bias_samples[0] - sj = self.bias_samples[1] + #if self.bias_samples is not None: + # si = self.bias_samples[0] + # sj = self.bias_samples[1] baoterm = self.BAO_term(z) kaiser_bsi = self.kaiserTerm(z, k, mu, bsi_z, bias_sample=si) kaiser_bsj = self.kaiserTerm(z, k, mu, bsj_z, bias_sample=sj) - T_HI = self.Temperature(z) extra_shotnoise = 0.0 # Set to identically zero for the moment, otherwise self.extraPshot lorentzFoG = self.FingersOfGod(z, k, mu, mode="Lorentz") p_dd_DW = self.dewiggled_pdd(z, k, mu) - beam_damping_term_si = self.beta_SD(z, k, mu) if si == "I" else 1 - beam_damping_term_sj = self.beta_SD(z, k, mu) if sj == "I" else 1 extra_shotnoise_si = np.sqrt(extra_shotnoise) if si == "g" else 0 extra_shotnoise_sj = np.sqrt(extra_shotnoise) if sj == "g" else 0 error_z_si = error_z if si == "g" else 1 @@ -1016,6 +979,6 @@ def observed_P_HI(self, z, k, mu, bsi_z=None, bsj_z=None, si="I", sj="I"): return p_obs - def lnpobs_IM(self, z, k, mu, bsi_z=None, bsj_z=None): - pobs = self.observed_P_HI(z, k, mu, bsi_z=bsi_z, bsj_z=bsj_z) + def lnpobs_ij(self, z, k, mu, bsi_z=None, bsj_z=None, si="I", sj="g"): + pobs = self.observed_P_ij(z, k, mu, bsi_z=bsi_z, bsj_z=bsj_z, si=si, sj=sj) return np.log(pobs) diff --git a/cosmicfishpie/configs/config.py b/cosmicfishpie/configs/config.py index 7c7ab46..2d54b11 100644 --- a/cosmicfishpie/configs/config.py +++ b/cosmicfishpie/configs/config.py @@ -14,7 +14,6 @@ from cosmicfishpie.utilities.utils import physmath as upm from cosmicfishpie.utilities.utils import printing as upt - def init( options=dict(), specifications=dict(), @@ -84,10 +83,6 @@ def init( Name of the survey specifications file for a photometric probe survey_name_spectro : str Name of the survey specifications file for a spectrocopic probe - survey_name_radio_photo : str - Name of the survey specifications file for a photometric radio probe - survey_name_radio_spectro : str - Name of the survey specifications file for a spectrocopic radio probe survey_name_radio_IM : str Name of the survey specifications file for a line intensity mapping probe derivatives : str @@ -222,8 +217,8 @@ def init( settings.setdefault("survey_specs", "ISTF-Optimistic") settings.setdefault("survey_name_photo", "Euclid-Photometric-ISTF-Pessimistic") settings.setdefault("survey_name_spectro", "Euclid-Spectroscopic-ISTF-Pessimistic") - settings.setdefault("survey_name_radio_photo", "SKA1-Photometric-Redbook-Optimistic") - settings.setdefault("survey_name_radio_spectro", "SKA1-Spectroscopic-Redbook-Optimistic") + #settings.setdefault("survey_name_radio_photo", "SKA1-Photometric-Redbook-Optimistic") + #settings.setdefault("survey_name_radio_spectro", "SKA1-Spectroscopic-Redbook-Optimistic") settings.setdefault("survey_name_radio_IM", "SKA1-IM-Redbook-Optimistic") settings.setdefault("fail_on_specs_not_found", False) settings.setdefault("derivatives", "3PT") @@ -369,11 +364,14 @@ def ngal_per_bin(ngal_sqarmin, zbins): ############################## # Add additional surveys here - available_survey_names = ["Euclid", "SKA1", "DESI", "Planck", "Rubin"] + available_survey_names = ["Euclid", "SKAO", "DESI", "Planck", "Rubin"] def create_ph_dict(foldername, filename): photo_dict = dict() - + if filename==False: + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> No photo survey passed, returning empty dict") + return photo_dict try: ph_file_path = os.path.join(foldername, filename + ".yaml") if not os.path.isfile(ph_file_path): @@ -404,8 +402,12 @@ def create_ph_dict(foldername, filename): return photo_dict - def create_sp_dict(foldername, filename): + def create_sp_dict(foldername, filename, type="spectro"): spec_dict = dict() + if filename==False: + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> No {type} survey passed, returning empty dict") + return spec_dict try: sp_file_path = os.path.join(foldername, filename + ".yaml") if not os.path.isfile(sp_file_path): @@ -437,74 +439,79 @@ def create_sp_dict(foldername, filename): global specs specs = specs_defaults.copy() # Start with default dict + spectroTaken = False + photoTaken = False + specificationsf = dict() if "Euclid" in surveyName: - specificationsf = dict() surveyNameSpectro = settings.get("survey_name_spectro") if surveyNameSpectro: specificationsf1 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) specificationsf.update(specificationsf1) + spectroTaken = True + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> Survey loaded: {surveyNameSpectro}") surveyNamePhoto = settings.get("survey_name_photo") if surveyNamePhoto: specificationsf2 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) specificationsf.update(specificationsf2) - if "SKA1" in surveyName: - surveyNameRadio = settings.get("survey_name_radio") - ## TODO: Fix this and check this for radio, sp, ph and IM - yaml_file = open(os.path.join(settings["specs_dir"], surveyNameRadio + ".yaml")) - parsed_yaml_file = yaml.load(yaml_file, Loader=yaml.FullLoader) - specificationsf = parsed_yaml_file["specifications"] - z_bins = specificationsf["z_bins_ph"] - specificationsf["z_bins_ph"] = np.array(z_bins) - specificationsf["ngalbin"] = ngal_per_bin( - specificationsf["ngal_sqarmin"], specificationsf["z_bins_ph"] - ) - specificationsf["z0"] = specificationsf["zm"] / np.sqrt(2) - specificationsf["z0_p"] = 1.0 - specificationsf["IM_bins_file"] = "SKA1_IM_MDB1_Redbook.dat" - specificationsf["IM_THI_noise_file"] = "SKA1_THI_sys_noise.txt" - specificationsf["binrange"] = range(1, len(specificationsf["z_bins_ph"])) - specificationsf["survey_name"] = surveyName - + photoTaken = True + upt.time_print(feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNamePhoto}") + if "SKA" in surveyName: + surveyNameRadioIM = settings.get("survey_name_radio_IM") + specificationsf3 = create_sp_dict(settings["specs_dir"], surveyNameRadioIM, type="IM") + specificationsf.update(specificationsf3) + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> Survey loaded: {surveyNameRadioIM}") + if spectroTaken==False: + surveyNameSpectro = settings.get("survey_name_spectro") + specificationsf4 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) + specificationsf.update(specificationsf4) + spectroTaken = True + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> Survey loaded: {surveyNameSpectro}") + if photoTaken==False: + surveyNamePhoto = settings.get("survey_name_photo") + specificationsf5 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) + specificationsf.update(specificationsf5) + photoTaken = True + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> Survey loaded: {surveyNamePhoto}") if "Rubin" in surveyName: - ## TODO: Fix this and check this for Rubin ph - if surveyName == "Rubin": - surveyName = "Rubin-Optimistic" - yaml_file = open(os.path.join(settings["specs_dir"], surveyName + ".yaml")) - parsed_yaml_file = yaml.load(yaml_file, Loader=yaml.FullLoader) - specificationsf = parsed_yaml_file["specifications"] - z_bins = specificationsf["z_bins"] - specificationsf["z_bins"] = np.array(z_bins) - specificationsf["ngalbin"] = ngal_per_bin( - specificationsf["ngal_sqarmin"], specificationsf["z_bins"] - ) - specificationsf["z0"] = specificationsf["zm"] / np.sqrt(2) - specificationsf["z0_p"] = 1.0 - specificationsf["binrange"] = range(1, len(specificationsf["z_bins"])) - specificationsf["survey_name"] = surveyName - print("Survey loaded: ", surveyName) - + if photoTaken==False: + surveyNamePhoto = settings.get("survey_name_photo") + specificationsf6 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) + specificationsf.update(specificationsf6) + photoTaken = True + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> Survey loaded: {surveyNamePhoto}") if "DESI" in surveyName: - ## TODO: Fix this and check this for DESI sp - yaml_file = open(os.path.join(settings["specs_dir"], "DESI-Optimistic.yaml")) - parsed_yaml_file = yaml.load(yaml_file, Loader=yaml.FullLoader) - specificationsf = parsed_yaml_file["specifications"] - # Only needed specs are loaded in nuisance.py - specificationsf["survey_name"] = surveyName - + if spectroTaken==False: + surveyNameSpectro = settings.get("survey_name_spectro") + specificationsf7 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) + specificationsf.update(specificationsf7) + spectroTaken = True + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> Survey loaded: {surveyNameSpectro}") if "Planck" in surveyName: yaml_file = open(os.path.join(settings["specs_dir"], "Planck.yaml")) parsed_yaml_file = yaml.load(yaml_file, Loader=yaml.FullLoader) - specificationsf = parsed_yaml_file["specifications"] - + specificationsfPlanck = parsed_yaml_file["specifications"] + specificationsf.update(specificationsfPlanck) + upt.time_print(feedback_level=feed_lvl, min_level=1, + text=f"-> Survey loaded: Planck") if surveyName not in available_survey_names: print("Survey name passed: ", surveyName) print( "Survey name not found in available survey names.", "Please pass your full custom specifications as a dictionary.", ) - - ums.deepupdate(specs, specificationsf) # deep update keys if present in files + upt.debug_print("Files specifications: ", specificationsf) + upt.debug_print("Default specifications: ", specs) + # ums.deepupdate(specs, specificationsf) # deep update keys if present in files + # no more deepupdate because it causes problems with duplicated keys in bias parameters when using different bias samples + specs.update(specificationsf) + upt.debug_print("Updated specifications: ", specs) specs["fsky_GCph"] = specificationsf.get( "fsky_GCph", upm.sqdegtofsky(specificationsf.get("area_survey_GCph", 0.0)) ) @@ -514,7 +521,11 @@ def create_sp_dict(foldername, filename): specs["fsky_spectro"] = specificationsf.get( "fsky_spectro", upm.sqdegtofsky(specificationsf.get("area_survey_spectro", 0.0)) ) - ums.deepupdate(specs, specifications) # deep update keys if passed by users + specs["fsky_IM"] = specificationsf.get( + "fsky_IM", upm.sqdegtofsky(specificationsf.get("area_survey_IM", 0.0)) + ) + #ums.deepupdate(specs, specifications) # deep update keys if passed by users + specs.update(specifications) specs["survey_name"] = surveyName specs["specs_dir"] = settings["specs_dir"] # Path for additional files like luminosity @@ -650,7 +661,7 @@ def create_sp_dict(foldername, filename): global Spectrononlinearparams Spectrononlinearparams = dict() - if "GCsp" in obs: + if "GCsp" in obs or "IM" in obs: gscp_nonlin_model = specs.get("nonlinear_model", "default") if gscp_nonlin_model == "default": Spectrononlinearparams = {} @@ -669,7 +680,7 @@ def create_sp_dict(foldername, filename): if PShotpars is not None: PShotparams = deepcopy(PShotpars) else: - if "GCsp" in obs: + if "GCsp" in obs or "IM" in obs: bias_model = specs["sp_bias_model"] bias_sample = specs["sp_bias_sample"] bias_prtz = specs["sp_bias_parametrization"] @@ -681,7 +692,11 @@ def create_sp_dict(foldername, filename): print("Warning: bias_sample not found in bias_parameter keys") shot_noise_model = specs["shot_noise_model"] shot_noise_prtz = specs["shot_noise_parametrization"] - for key in shot_noise_prtz[shot_noise_model].keys(): + try: + Pskeys = shot_noise_prtz[shot_noise_model].keys() + except AttributeError: + Pskeys = [] + for key in Pskeys: PShotparams[key] = shot_noise_prtz[shot_noise_model][key] global IMbiasparams @@ -690,27 +705,28 @@ def create_sp_dict(foldername, filename): IMbiasparams = deepcopy(IMbiaspars) else: if "IM" in obs: - bias_model = specs["im_bias_model"] - bias_prtz = specs["im_bias_parametrization"] - for key in bias_prtz[bias_model].keys(): - IMbiasparams[key] = bias_prtz[bias_model][key] + bias_model = specs["IM_bias_model"] + bias_sample = specs["IM_bias_sample"] + bias_prtz = specs["IM_bias_parametrization"] + bias_prmod = deepcopy(bias_prtz[bias_model]) + for key in bias_prmod.keys(): + IMbiasparams[key] = bias_prmod[key] # Set the default free parameters for the spectro nuisance parameters - if "GCsp" in obs: + if "GCsp" in obs or "IM" in obs: default_eps_gc_nuis = settings["eps_gal_nuispars"] default_eps_gc_nonlin = settings["eps_gal_nonlinpars"] - # Only add the free parameters that are not already in the dictionary - for key in Spectrobiasparams: - freeparams.setdefault(key, default_eps_gc_nuis) - upt.debug_print(freeparams) - # if "IM" not in obs: for key in PShotparams: freeparams.setdefault(key, default_eps_gc_nuis) for key in Spectrononlinearparams: - freeparams.setdefault(key, default_eps_gc_nonlin) - if "IM" in obs: - for key in IMbiasparams: - freeparams.setdefault(key, default_eps_gc_nuis) + freeparams.setdefault(key, default_eps_gc_nonlin) + # Only add the free parameters that are not already in the dictionary + if "GCsp" in obs: + for key in Spectrobiasparams: + freeparams.setdefault(key, default_eps_gc_nuis) + if "IM" in obs: + for key in IMbiasparams: + freeparams.setdefault(key, default_eps_gc_nuis) global latex_names latex_names_def = { diff --git a/cosmicfishpie/cosmology/nuisance.py b/cosmicfishpie/cosmology/nuisance.py index 9f481bc..5cafbe9 100644 --- a/cosmicfishpie/cosmology/nuisance.py +++ b/cosmicfishpie/cosmology/nuisance.py @@ -33,7 +33,8 @@ class Nuisance: - def __init__(self, configuration=None, spectrobiasparams=None, spectrononlinearpars=None): + def __init__(self, configuration=None, spectrobiasparams=None, + spectrononlinearpars=None, IMbiasparams=None): if configuration is None: self.config = cfg else: @@ -43,7 +44,7 @@ def __init__(self, configuration=None, spectrobiasparams=None, spectrononlinearp self.settings = self.config.settings self.specsdir = self.settings["specs_dir"] self.surveyname = self.specs["survey_name"] - if "GCsp" in self.observables: + if "GCsp" in self.observables or "IM" in self.observables: self.sp_zbins = self.gcsp_zbins() self.sp_dndz = self.gcsp_dndz() self.sp_zbins_mids = self.gcsp_zbins_mids() @@ -55,18 +56,31 @@ def __init__(self, configuration=None, spectrobiasparams=None, spectrononlinearp self.Spectrobiasparams = deepcopy(self.config.Spectrobiasparams) else: self.Spectrobiasparams = spectrobiasparams + self._vectorized_gcsp_bias_at_z = np.vectorize(self.gcsp_bias_at_z) + if "IM" in self.observables: + self.IM_zbins = self.IM_zbins_func() + self.IM_zbins_mids = self.IM_zbins_mids_func() + self.IM_bias_sample = self.specs["IM_bias_sample"] + self.IM_bias_root = self.specs["IM_bias_root"] + self.IM_bias_model = self.specs["IM_bias_model"] + self.IM_bias_prtz = self.specs["IM_bias_parametrization"] + if IMbiasparams is None: + self.IMbiasparams = deepcopy(self.config.IMbiasparams) + else: + self.IMbiasparams = IMbiasparams + if self.IM_bias_model == "fitting": + self.IM_bias_at_z = self.IM_bias_fitting + else: + print("Not implemented bias model for IM") + raise ValueError(f"IM bias model {self.IM_bias_model} not implemented") + if "GCsp" in self.observables or "IM" in self.observables: if spectrononlinearpars is None: self.spectrononlinearpars = deepcopy(self.config.Spectrononlinearparams) else: self.spectrononlinearpars = spectrononlinearpars - self._vectorized_gcsp_bias_at_z = np.vectorize(self.gcsp_bias_at_z) self._vectorized_gcsp_rescale_sigmapv_at_z = np.vectorize( self.gcsp_rescale_sigmapv_at_z, excluded=["sigma_key"] ) - - if "IM" in self.observables: - filename_THI_noise = self.specs["IM_THI_noise_file"] - self.Tsys_arr = np.loadtxt(os.path.join(self.specsdir, filename_THI_noise)) if "WL" in self.observables: self.lumratio = self.luminosity_ratio() if "GCph" in self.observables or "WL" in self.observables: @@ -332,35 +346,55 @@ def extra_Pshot_noise(self): Psfid = self.settings["Pshot_nuisance_fiducial"] = 0 return Psfid - def IM_bias(self, z): + def IM_zbins_func(self): + """ + Reads from file for a given survey + """ + zbins = [] + zbin_inds = [] + for key, val in self.specs["z_bins_IM"].items(): + zbins.append(val) + zbin_inds.append(key) + zbins = np.unique(np.concatenate(zbins)) + self.IM_zbins_inds = zbin_inds + return zbins + + def IM_zbins_mids_func(self): + IM_zbins_mids = unu.moving_average(self.IM_zbins) + return IM_zbins_mids + + def IM_bias_fitting(self, z): """ IM 21cm HI bias function from http://arxiv.org/abs/2006.05996 """ - bb = 0.3 * (1 + z) + 0.6 + c1 = self.IMbiasparams["bI_c1"] + c2 = self.IMbiasparams["bI_c2"] + bb = c1 * (1 + z) + c2 return bb - def IM_zbins(self): - """ - Reads from file for a given survey - """ - # this dict can be read from a file - zbins = np.unique(np.concatenate((self.im_table[:, 0], self.im_table[:, 2]))) - return zbins + def IM_THI_noise(self): + """Get the system noise temperature interpolation function for Intensity Mapping. - def IM_zbins_mids(self): - z_bins = self.IM_zbins() - z_bin_mids = unu.moving_average(z_bins) - return z_bin_mids + This function creates an interpolation of the system noise temperature (T_sys) + as a function of redshift for HI intensity mapping observations. - def IM_bias_at_zm(self): - bfunc = self.IM_bias - zmids = self.IM_zbins_mids() - b_arr = bfunc(zmids) - return b_arr + Returns + ------- + scipy.interpolate.UnivariateSpline + Interpolation function that takes redshift as input and returns the + corresponding system noise temperature value. - def IM_THI_noise(self): - """ " - Reads from file for a given survey + Notes + ----- + The function reads the system noise data from the survey specifications, + which should contain: + - z_vals_THI : array-like + Redshift values where the noise temperature is defined + - THI_sys_noise : array-like + System noise temperature values corresponding to the redshift points """ - Tsys_interp = UnivariateSpline(self.Tsys_arr[:, 0], self.Tsys_arr[:, 1]) + THI_sys = self.specs["THI_sys_noise"] + z_vals_THI = THI_sys['z_vals_THI'] + THI_vals = THI_sys['THI_sys_noise'] + Tsys_interp = UnivariateSpline(z_vals_THI, THI_vals) return Tsys_interp diff --git a/cosmicfishpie/fishermatrix/cosmicfish.py b/cosmicfishpie/fishermatrix/cosmicfish.py index 7659f6a..6054124 100644 --- a/cosmicfishpie/fishermatrix/cosmicfish.py +++ b/cosmicfishpie/fishermatrix/cosmicfish.py @@ -259,44 +259,34 @@ def compute(self, max_z_bins=None): self.set_pk_settings() if "IM" in self.observables and "GCsp" in self.observables: self.obs_spectrum = ["I", "g"] - self.pk_obs = spectro_obs.ComputeGalIM( - cosmopars=self.fiducialcosmopars, - fiducial_cosmopars=self.fiducialcosmopars, - bias_samples=self.obs_spectrum, - configuration=self, - ) - - elif "IM" in self.observables: + elif "IM" in self.observables: # compute in the case of IM only self.obs_spectrum = ["I", "I"] - self.pk_obs = spectro_obs.ComputeGalIM( - cosmopars=self.fiducialcosmopars, - fiducial_cosmopars=self.fiducialcosmopars, - bias_samples=self.obs_spectrum, - configuration=self, - ) elif "GCsp" in self.observables: self.obs_spectrum = ["g", "g"] - self.pk_obs = spectro_obs.ComputeGalSpectro( - cosmopars=self.fiducialcosmopars, - fiducial_cosmopars=self.fiducialcosmopars, - spectrobiaspars=self.Spectrobiaspars, - spectrononlinearpars=self.Spectrononlinpars, - PShotpars=self.PShotpars, - bias_samples=self.obs_spectrum, - configuration=self, - ) - + # self.obs_spectrum = ["I", "g"] + self.pk_obs_fid = spectro_obs.ComputeGalSpectro( + cosmopars=self.fiducialcosmopars, + fiducial_cosmopars=self.fiducialcosmopars, + spectrobiaspars=self.Spectrobiaspars, + spectrononlinearpars=self.Spectrononlinpars, + IMbiaspars=self.IMbiaspars, + PShotpars=self.PShotpars, + # bias_samples=self.obs_spectrum, + configuration=self, + ) self.pk_cov = spectro_cov.SpectroCov( self.fiducialcosmopars, - fiducial_specobs=self.pk_obs, + fiducial_specobs=self.pk_obs_fid, bias_samples=self.obs_spectrum, configuration=self, ) - self.zmids = self.pk_cov.global_z_bin_mids + self.zmids = self.pk_cov.inter_z_bin_mids nbins = len(self.zmids) - if max_z_bins is None: - max_z_bins = nbins - self.eliminate_zbinned_freepars(max_z_bins) + if max_z_bins is not None and type(max_z_bins) == int: + cutnbins = max_z_bins + self.eliminate_zbinned_freepars(cutnbins) + self.zmids = self.zmids[0:cutnbins] + nbins = len(self.zmids) tini = time() self.derivs_dict = dict() # if self.parallel == False: @@ -304,16 +294,14 @@ def compute(self, max_z_bins=None): mu_mesh = self.Pk_mumesh self.veff_arr = [] tvi = time() - self.fish_z_arr = np.empty(max_z_bins) - for ibi in range(max_z_bins): - self.fish_z_arr[ibi] = self.zmids[ibi] + self.fish_z_arr = np.array(self.zmids) + for zi in self.zmids: if "IM" in self.observables and "GCsp" in self.observables: - # compute in the case of IMxGC - self.veff_arr.append(self.pk_cov.veff_XC(ibi, k_mesh, mu_mesh)) + self.veff_arr.append(self.pk_cov.veff_Ig(zi, k_mesh, mu_mesh)) elif "IM" in self.observables: # compute in the case of IM only - self.veff_arr.append(self.pk_cov.veff_21cm(ibi, k_mesh, mu_mesh)) + self.veff_arr.append(self.pk_cov.veff_II(zi, k_mesh, mu_mesh)) elif "GCsp" in self.observables: - self.veff_arr.append(self.pk_cov.veff(ibi, k_mesh, mu_mesh)) + self.veff_arr.append(self.pk_cov.veff(zi, k_mesh, mu_mesh)) tvf = time() upt.time_print( feedback_level=self.feed_lvl, @@ -334,7 +322,7 @@ def compute(self, max_z_bins=None): instance=self, ) self.allparams = self.pk_deriv.fiducial_allpars - pk_Fish = self.pk_LSS_Fisher(nbins=max_z_bins) + pk_Fish = self.pk_LSS_Fisher(nbins=nbins) specFM = np.sum(pk_Fish, axis=0) finalFisher = deepcopy(specFM) @@ -383,8 +371,8 @@ def set_pk_settings(self): k_units_factor = self.fiducialcosmopars["h"] self.kmin_fish = self.specs["kmin_GCsp"] * k_units_factor self.kmax_fish = self.specs["kmax_GCsp"] * k_units_factor - self.Pk_ksamp = 2049 * self.settings["accuracy"] - self.Pk_musamp = 129 * self.settings["accuracy"] + self.Pk_ksamp = 513 * self.settings["accuracy"] + self.Pk_musamp = 9 * self.settings["accuracy"] self.Pk_kgrid = np.linspace(self.kmin_fish, self.kmax_fish, self.Pk_ksamp) self.Pk_mugrid = np.linspace(0.0, 1.0, self.Pk_musamp) self.Pk_kmesh, self.Pk_mumesh = np.meshgrid(self.Pk_kgrid, self.Pk_mugrid) @@ -406,7 +394,7 @@ def compute_binned_derivs(self, z_arr): z_arr, self.Pk_kmesh, self.Pk_mumesh, - fiducial_spectro_obj=self.pk_obs, + fiducial_spectro_obj=self.pk_obs_fid, bias_samples=self.obs_spectrum, configuration=self, ) @@ -827,12 +815,11 @@ def eliminate_zbinned_freepars(self, nmax): if "_" in key: # convention for z-binned pars: par_ibin keysplit = key.split("_") - ibin = int(keysplit[1]) try: ibin = int(keysplit[1]) except ValueError: continue - if ibin > nmax: + if type(ibin) == int and ibin > nmax: removedpar = self.freeparams.pop(key) upt.time_print( feedback_level=self.feed_lvl, From 32b920b5d659e411d74bb75db793f8911f8f500a Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Fri, 8 Nov 2024 18:55:52 +0100 Subject: [PATCH 2/8] simple plotting function --- cosmicfishpie/analysis/fishconsumer.py | 85 ++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) diff --git a/cosmicfishpie/analysis/fishconsumer.py b/cosmicfishpie/analysis/fishconsumer.py index b030871..17bac5a 100644 --- a/cosmicfishpie/analysis/fishconsumer.py +++ b/cosmicfishpie/analysis/fishconsumer.py @@ -775,3 +775,88 @@ def chainfishplot( fig.savefig(plotfilename, dpi=save_dpi, bbox_inches="tight") print("Plot saved to: ", plotfilename) return fig + +def simple_fisher_plot( + fisher_list, + params_to_plot, + labels=None, + colors=None, + save_plot=False, + legend=True, + n_samples=10000, + output_file="fisher_plot.pdf" +): + """Create a triangle plot from Fisher matrices using ChainConsumer. + + Parameters + ---------- + fisher_list : list + List of CosmicFish_FisherMatrix objects to plot + params_to_plot : list + List of parameter names to include in the plot + labels : list, optional + Labels for each Fisher matrix in the legend + colors : list, optional + Colors for each Fisher matrix. Defaults to built-in colors + save_plot : bool, optional + Whether to save the plot to file (default: False) + output_file : str, optional + Filename for saving the plot (default: 'fisher_plot.pdf') + + Returns + ------- + matplotlib.figure.Figure + The generated triangle plot figure + """ + # Initialize ChainConsumer + c = ChainConsumer() + + # Default colors if none provided + if colors is None: + colors = ['#3a86ff', '#fb5607', '#8338ec', '#ffbe0b', '#d11149'] + colors = colors[:len(fisher_list)] # Truncate to needed length + + # Default labels if none provided + if labels is None: + labels = [f"Fisher {i+1}" for i in range(len(fisher_list))] + + # Generate samples for each Fisher matrix + n_samples = 100000 + for i, fisher in enumerate(fisher_list): + # Get samples from multivariate normal using Fisher matrix + samples = multivariate_normal( + fisher.param_fiducial, + fisher.inverse_fisher_matrix(), + size=n_samples + ) + + # Add chain to plot + c.add_chain( + samples, + parameters=fisher.get_param_names(), + name=labels[i], + color=colors[i] + ) + + # Configure plot settings + c.configure( + plot_hists=True, + sigma2d=False, + smooth=3, + colors=colors, + shade=True, + shade_alpha=0.3, + bar_shade=True, + linewidths=2, + legend_kwargs={"fontsize": 12} + ) + + # Create the plot + fig = c.plotter.plot(parameters=params_to_plot, legend=legend) + + # Save if requested + if save_plot: + fig.savefig(output_file, bbox_inches='tight', dpi=200) + print(f"Plot saved to: {output_file}") + + return fig From ce19f3326aebe9408584cd7077d787137e6d47a2 Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Fri, 8 Nov 2024 18:58:38 +0100 Subject: [PATCH 3/8] configs for SKA and other surveys --- .../Euclid-Spectroscopic-DR1-Pessimistic.yaml | 58 +++++++++++++ ...ectroscopic-ISTF-Pessimistic-sigma_pv.yaml | 58 +++++++++++++ .../MeerKlass-IM-Ze.yaml | 75 +++++++++++++++++ .../SKAO-IM-Redbook.yaml | 82 +++++++++++++++++++ .../SKAO-Photometric-Redbook.yaml | 43 ++++++++++ .../SKAO-Spectroscopic-Redbook.yaml | 57 +++++++++++++ 6 files changed, 373 insertions(+) create mode 100644 cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-DR1-Pessimistic.yaml create mode 100644 cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv.yaml create mode 100644 cosmicfishpie/configs/other_survey_specifications/MeerKlass-IM-Ze.yaml create mode 100644 cosmicfishpie/configs/other_survey_specifications/SKAO-IM-Redbook.yaml create mode 100644 cosmicfishpie/configs/other_survey_specifications/SKAO-Photometric-Redbook.yaml create mode 100644 cosmicfishpie/configs/other_survey_specifications/SKAO-Spectroscopic-Redbook.yaml diff --git a/cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-DR1-Pessimistic.yaml b/cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-DR1-Pessimistic.yaml new file mode 100644 index 0000000..147a1c8 --- /dev/null +++ b/cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-DR1-Pessimistic.yaml @@ -0,0 +1,58 @@ +specifications: + 'spec_sigma_dz': 0.002 + 'area_survey_spectro': 2500 + 'kmax_GCsp': 0.15 + 'kmin_GCsp': 0.001 + 'z_bins_sp': + 1: [0.90, 1.10] + 2: [1.10, 1.30] + 3: [1.30, 1.50] + 4: [1.50, 1.80] + 'dndOmegadz' : + 1: 1815.0 + 2: 1701.5 + 3: 1410.0 + 4: 940.97 + sp_bias_sample: 'g' + sp_bias_model: 'linear_log' + sp_bias_root: 'lnb' + sp_bias_parametrization: + linear: + bg_1: 1.4614804 + bg_2: 1.6060949 + bg_3: 1.7464790 + bg_4: 1.8988660 + linear_log: + lnbg_1: 0.37944989 + lnbg_2: 0.4738057 + lnbg_3: 0.55760176 + lnbg_4: 0.64125687 + linear_Qbias: + bg_1: 1.4614804 + bg_2: 1.6060949 + bg_3: 1.7464790 + bg_4: 1.8988660 + A1: 0.02 + A2: -2.0 + shot_noise_model: 'fixed' #'constant' + shot_noise_parametrization: + fixed: + constant: + Ps_1: 0.0 + Ps_2: 0.0 + Ps_3: 0.0 + Ps_4: 0.0 + nonlinear_model: default #'rescale_sigma_pv' # 'default' or 'rescale_sigma_pv' + nonlinear_parametrization: + vary_sigmap: False #True + vary_sigmav: False #True + rescale_sigma_pv: + sigmap_1: 1.0 + sigmap_2: 1.0 + sigmap_3: 1.0 + sigmap_4: 1.0 + sigmav_1: 1.0 + sigmav_2: 1.0 + sigmav_3: 1.0 + sigmav_4: 1.0 + default: \ No newline at end of file diff --git a/cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv.yaml b/cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv.yaml new file mode 100644 index 0000000..e7b9cba --- /dev/null +++ b/cosmicfishpie/configs/other_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv.yaml @@ -0,0 +1,58 @@ +specifications: + 'spec_sigma_dz': 0.002 + 'area_survey_spectro': 15000 + 'kmax_GCsp': 0.25 + 'kmin_GCsp': 0.001 + 'z_bins_sp': + 1: [0.90, 1.10] + 2: [1.10, 1.30] + 3: [1.30, 1.50] + 4: [1.50, 1.80] + 'dndOmegadz' : + 1: 1815.0 + 2: 1701.5 + 3: 1410.0 + 4: 940.97 + sp_bias_sample: 'g' + sp_bias_model: 'linear_log' + sp_bias_root: 'lnb' + sp_bias_parametrization: + linear: + bg_1: 1.4614804 + bg_2: 1.6060949 + bg_3: 1.7464790 + bg_4: 1.8988660 + linear_log: + lnbg_1: 0.37944989 + lnbg_2: 0.4738057 + lnbg_3: 0.55760176 + lnbg_4: 0.64125687 + linear_Qbias: + bg_1: 1.4614804 + bg_2: 1.6060949 + bg_3: 1.7464790 + bg_4: 1.8988660 + A1: 0.02 + A2: -2.0 + shot_noise_model: 'fixed' #'constant' + shot_noise_parametrization: + fixed: + constant: + Ps_1: 0.0 + Ps_2: 0.0 + Ps_3: 0.0 + Ps_4: 0.0 + nonlinear_model: default #'rescale_sigma_pv' # 'default' or 'rescale_sigma_pv' + nonlinear_parametrization: + vary_sigmap: False #True + vary_sigmav: False #True + rescale_sigma_pv: + sigmap_1: 1.0 + sigmap_2: 1.0 + sigmap_3: 1.0 + sigmap_4: 1.0 + sigmav_1: 1.0 + sigmav_2: 1.0 + sigmav_3: 1.0 + sigmav_4: 1.0 + default: \ No newline at end of file diff --git a/cosmicfishpie/configs/other_survey_specifications/MeerKlass-IM-Ze.yaml b/cosmicfishpie/configs/other_survey_specifications/MeerKlass-IM-Ze.yaml new file mode 100644 index 0000000..c57f917 --- /dev/null +++ b/cosmicfishpie/configs/other_survey_specifications/MeerKlass-IM-Ze.yaml @@ -0,0 +1,75 @@ +specifications: + 'IM_sigma_dz': 0.0001 + 'area_survey_IM': 7500 + 'kmax_IM': 0.1 + 'kmin_IM': 0.001 + 'D_dish': 13 + 'time_tot': 10000 ## in hr + 'N_dish': 60 + 'z_bins_IM': + 1: [0.40, 0.60] + 2: [0.60, 0.80] + 3: [0.80, 1.00] + 4: [1.00, 1.20] + Omega_HI_params: # O_c1*(1 + z)**O_c2 * O_scale + O_c1: 4.0 + O_c2: 0.6 + O_scale: 0.0001 + IM_bias_sample: 'I' + IM_bias_model: 'fitting' #'linear' #'fitting' + IM_bias_root: 'bH' + IM_bias_parametrization: + fitting: # bI = bI_c1 * (1 + z) + bI_c2 + bI_c1: 0.3 + bI_c2: 0.6 + linear: + bHI_1: 1.08 + bHI_2: 1.14 + bHI_3: 1.2 + bHI_4: 1.26 + bHI_5: 1.32 + bHI_6: 1.395 + bHI_7: 1.47 + bHI_8: 1.53 + bHI_9: 1.59 + bHI_10: 1.65 + linear_log: + linear_Qbias: + bg_1: 0.64813367 + bg_2: 0.71751723 + bg_3: 0.79432839 + bg_4: 0.87936228 + bg_4: 0.97349916 + A1: 0.02 + A2: -2.0 + shot_noise_model: 'fixed' #'constant' + shot_noise_parametrization: + fixed: + constant: + Ps_1: 0.0 + Ps_2: 0.0 + Ps_3: 0.0 + Ps_4: 0.0 + Ps_5: 0.0 + Ps_6: 0.0 + Ps_7: 0.0 + Ps_8: 0.0 + Ps_9: 0.0 + Ps_10: 0.0 + nonlinear_model: 'default' #'rescale_sigma_pv' # 'default' or 'rescale_sigma_pv' + nonlinear_parametrization: + vary_sigmap: True + vary_sigmav: True + rescale_sigma_pv: + sigmap_1: 1.0 + sigmap_2: 1.0 + sigmap_3: 1.0 + sigmap_4: 1.0 + sigmav_1: 1.0 + sigmav_2: 1.0 + sigmav_3: 1.0 + sigmav_4: 1.0 + default: + THI_sys_noise: + z_vals_THI: [0.403, 0.470, 0.539, 0.612, 0.767, 0.850, 0.938, 1.03, 1.12, 1.22, 1.33, 1.44, 1.55, 1.67, 1.80, 1.93, 2.07, 2.22, 2.37, 2.54, 2.69, 2.87, 3.05] + THI_sys_noise: [27.2, 26.9, 26.8, 26.9, 27.5, 28.1, 28.8, 29.8, 30.8, 32.1, 33.5, 35.2, 37.1, 39.2, 41.6, 44.2, 47.2, 50.6, 54.4, 58.6, 63.4, 68.8, 74.8] \ No newline at end of file diff --git a/cosmicfishpie/configs/other_survey_specifications/SKAO-IM-Redbook.yaml b/cosmicfishpie/configs/other_survey_specifications/SKAO-IM-Redbook.yaml new file mode 100644 index 0000000..d7c72cf --- /dev/null +++ b/cosmicfishpie/configs/other_survey_specifications/SKAO-IM-Redbook.yaml @@ -0,0 +1,82 @@ +specifications: + 'IM_sigma_dz': 0.0001 + 'area_survey_IM': 5000 + 'kmax_IM': 0.15 + 'kmin_IM': 0.001 + 'D_dish': 15 + 'fsky_IM': 0.48 + 'time_tot': 10000 ## in hr + 'N_dish': 197 + 'z_bins_IM': + 1: [0.50, 0.70] + 2: [0.70, 0.90] + 3: [0.90, 1.10] + 4: [1.10, 1.30] + 5: [1.30, 1.50] + 6: [1.50, 1.80] + 7: [1.80, 2.00] + 8: [2.00, 2.20] + 9: [2.20, 2.40] + 10: [2.40, 2.60] + Omega_HI_params: # O_c1*(1 + z)**O_c2 * O_scale + O_c1: 4.0 + O_c2: 0.6 + O_scale: 0.0001 + IM_bias_sample: 'I' + IM_bias_model: 'fitting' #'linear' #'fitting' + IM_bias_root: 'bH' + IM_bias_parametrization: + fitting: # bI = bI_c1 * (1 + z) + bI_c2 + bI_c1: 0.3 + bI_c2: 0.6 + linear: + bHI_1: 1.08 + bHI_2: 1.14 + bHI_3: 1.2 + bHI_4: 1.26 + bHI_5: 1.32 + bHI_6: 1.395 + bHI_7: 1.47 + bHI_8: 1.53 + bHI_9: 1.59 + bHI_10: 1.65 + linear_log: + linear_Qbias: + bg_1: 0.64813367 + bg_2: 0.71751723 + bg_3: 0.79432839 + bg_4: 0.87936228 + bg_4: 0.97349916 + A1: 0.02 + A2: -2.0 + shot_noise_model: 'fixed' #'constant' + shot_noise_parametrization: + fixed: + constant: + Ps_1: 0.0 + Ps_2: 0.0 + Ps_3: 0.0 + Ps_4: 0.0 + Ps_5: 0.0 + Ps_6: 0.0 + Ps_7: 0.0 + Ps_8: 0.0 + Ps_9: 0.0 + Ps_10: 0.0 + nonlinear_model: 'default' #'rescale_sigma_pv' # 'default' or 'rescale_sigma_pv' + nonlinear_parametrization: + vary_sigmap: True + vary_sigmav: True + rescale_sigma_pv: + sigmap_1: 1.0 + sigmap_2: 1.0 + sigmap_3: 1.0 + sigmap_4: 1.0 + sigmav_1: 1.0 + sigmav_2: 1.0 + sigmav_3: 1.0 + sigmav_4: 1.0 + default: + THI_sys_noise: + z_vals_THI: [0.403, 0.470, 0.539, 0.612, 0.767, 0.850, 0.938, 1.03, 1.12, 1.22, 1.33, 1.44, 1.55, 1.67, 1.80, 1.93, 2.07, 2.22, 2.37, 2.54, 2.69, 2.87, 3.05] + THI_sys_noise: [27.2, 26.9, 26.8, 26.9, 27.5, 28.1, 28.8, 29.8, 30.8, 32.1, 33.5, 35.2, 37.1, 39.2, 41.6, 44.2, 47.2, 50.6, 54.4, 58.6, 63.4, 68.8, 74.8] \ No newline at end of file diff --git a/cosmicfishpie/configs/other_survey_specifications/SKAO-Photometric-Redbook.yaml b/cosmicfishpie/configs/other_survey_specifications/SKAO-Photometric-Redbook.yaml new file mode 100644 index 0000000..8aa1f4d --- /dev/null +++ b/cosmicfishpie/configs/other_survey_specifications/SKAO-Photometric-Redbook.yaml @@ -0,0 +1,43 @@ +specifications: + 'z_bins_ph': [0.001, 0.418, 0.560, 0.678, 0.789, 0.900, 1.019, 1.155, 1.324, 1.576, 2.50] + 'zm': 1.1 + 'ngamma': 1.25 + 'ngal_sqarmin': 2.7 + photo_z_params: + 'fout': 0.1 + 'co': 1 + 'cb': 1 + 'sigma_o': 0.05 + 'sigma_b': 0.05 + 'zo': 0.1 + 'zb': 0.0 + 'fsky_GCph': 0.1166 + 'lmax_GCph': 750 + 'lmin_GCph': 10 + 'ellipt_error': 0.3 + 'fsky_WL': 0.1166 + 'lmax_WL': 3000 + 'lmin_WL': 10 + 'kmax': 30. + 'vary_ph_bias': 0.06 + 'vary_IA_pars': 0.035 + ph_bias_model: 'binned' + ph_bias_parametrization: + binned: + keystr: 'b' + generate_bias_keys: true + binned_constant: + keystr: 'b' + generate_bias_keys: true + sqrt: + b0: 1.0 + flagship: + A: 1.0 + B: 2.5 + C: 2.8 + D: 1.6 + IA_params: + IA_model: 'eNLA' + AIA: 1.72 + betaIA: 2.17 + etaIA: -0.41 diff --git a/cosmicfishpie/configs/other_survey_specifications/SKAO-Spectroscopic-Redbook.yaml b/cosmicfishpie/configs/other_survey_specifications/SKAO-Spectroscopic-Redbook.yaml new file mode 100644 index 0000000..50c9796 --- /dev/null +++ b/cosmicfishpie/configs/other_survey_specifications/SKAO-Spectroscopic-Redbook.yaml @@ -0,0 +1,57 @@ +specifications: + 'spec_sigma_dz': 0.002 + 'area_survey_spectro': 5000 + 'kmax_GCsp': 0.25 + 'kmin_GCsp': 0.001 + 'z_bins_sp': + 1: [0.00, 0.10] + 2: [0.10, 0.20] + 3: [0.20, 0.30] + 4: [0.30, 0.40] + 5: [0.40, 0.50] + 'dndOmegadz' : + 1: 2714.82 + 2: 2722.37 + 3: 1264.0 + 4: 467.043 + 5: 154.353 + sp_bias_sample: 'g' + sp_bias_model: 'linear' + sp_bias_root: 'bH' ## for HI bias + sp_bias_parametrization: + linear: + bHg_1: 0.64813367 + bHg_2: 0.71751723 + bHg_3: 0.79432839 + bHg_4: 0.87936228 + bHg_5: 0.97349916 + linear_log: + linear_Qbias: + bHg_1: 0.64813367 + bHg_2: 0.71751723 + bHg_3: 0.79432839 + bHg_4: 0.87936228 + bHg_5: 0.97349916 + A1: 0.02 + A2: -2.0 + shot_noise_model: 'constant' + shot_noise_parametrization: + constant: + Ps_1: 0.0 + Ps_2: 0.0 + Ps_3: 0.0 + Ps_4: 0.0 + nonlinear_model: 'default' #'rescale_sigma_pv' # 'default' or 'rescale_sigma_pv' + nonlinear_parametrization: + vary_sigmap: True + vary_sigmav: True + rescale_sigma_pv: + sigmap_1: 1.0 + sigmap_2: 1.0 + sigmap_3: 1.0 + sigmap_4: 1.0 + sigmav_1: 1.0 + sigmav_2: 1.0 + sigmav_3: 1.0 + sigmav_4: 1.0 + default: \ No newline at end of file From c43e2c9113b5201e42b1aed5daf4d9ba88a0c5a9 Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Fri, 8 Nov 2024 19:04:21 +0100 Subject: [PATCH 4/8] SKA notebooks --- .gitignore | 5 +- notebooks/DR1+Meerklass_presentation.ipynb | 1220 ++++++++++++++++++++ notebooks/SKAO_presentation.ipynb | 1186 +++++++++++++++++++ 3 files changed, 2410 insertions(+), 1 deletion(-) create mode 100644 notebooks/DR1+Meerklass_presentation.ipynb create mode 100644 notebooks/SKAO_presentation.ipynb diff --git a/.gitignore b/.gitignore index ccb1839..ef18967 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# heavy data +*.hdf5 +*.pdf # build artifacts .eggs/ @@ -54,4 +57,4 @@ site/ # User Output plots/ -results/ \ No newline at end of file +results/ diff --git a/notebooks/DR1+Meerklass_presentation.ipynb b/notebooks/DR1+Meerklass_presentation.ipynb new file mode 100644 index 0000000..301f355 --- /dev/null +++ b/notebooks/DR1+Meerklass_presentation.ipynb @@ -0,0 +1,1220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import ListedColormap\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "snscolors = sns.color_palette(\"colorblind\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from cosmicfishpie.fishermatrix import cosmicfish\n", + "from cosmicfishpie.utilities.utils import printing as upt\n", + "upt.debug = False\n", + "upt.debug_print(\"Debug is on\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "fiducial = {\n", + " \"Omegam\": 0.32,\n", + " \"Omegab\": 0.05,\n", + " \"h\": 0.67,\n", + " \"ns\": 0.96,\n", + " \"sigma8\": 0.815584,\n", + " \"w0\": -1.0,\n", + " \"wa\": 0.0,\n", + " \"mnu\": 0.06,\n", + " \"Neff\": 3.044,\n", + "}\n", + "\n", + "options = {\n", + " \"accuracy\": 1,\n", + " \"feedback\": 1,\n", + " \"code\": \"symbolic\",\n", + " \"outroot\": \"GCsp_DR1+Meerklass\",\n", + " \"survey_name\": \"SKAO\",\n", + " #\"survey_name_spectro\": \"SKAO-Spectroscopic-Redbook\",\n", + " \"survey_name_spectro\": \"Euclid-Spectroscopic-DR1-Pessimistic\",\n", + " \"survey_name_photo\": False,\n", + " \"survey_name_radio_IM\": \"MeerKlass-IM-Ze\",\n", + " 'specs_dir': '../cosmicfishpie/configs/other_survey_specifications/',\n", + " \"cosmo_model\": \"LCDM\",\n", + " \"bfs8terms\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: MeerKlass-IM-Ze\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-DR1-Pessimistic\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.11 s\n" + ] + } + ], + "source": [ + "observables = [\"GCsp\", \"IM\"]\n", + "cosmoFM_A = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spectroscopic Power Spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Omegam': 0.01,\n", + " 'Omegab': 0.01,\n", + " 'h': 0.01,\n", + " 'ns': 0.01,\n", + " 'sigma8': 0.01,\n", + " 'lnbg_1': 0.0001,\n", + " 'lnbg_2': 0.0001,\n", + " 'lnbg_3': 0.0001,\n", + " 'lnbg_4': 0.0001,\n", + " 'bI_c1': 0.0001,\n", + " 'bI_c2': 0.0001}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosmoFM_A.freeparams" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from cosmicfishpie.LSSsurvey import spectro_obs as spobs\n", + "from cosmicfishpie.LSSsurvey import spectro_cov as spcov\n", + "\n", + "spectro_Pk = spobs.ComputeGalSpectro(cosmoFM_A.fiducialcosmopars)\n", + "spectro_Cov = spcov.SpectroCov(cosmoFM_A.fiducialcosmopars, \n", + " fiducial_specobs=spectro_Pk)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['GCsp', 'IM']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectro_Pk.observables" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IM zbins: [0.4 0.6 0.8 1. 1.2]\n", + "IM zbins mids: [0.5 0.7 0.9 1.1]\n", + "IM bias pars: {'bI_c1': 0.3, 'bI_c2': 0.6}\n" + ] + } + ], + "source": [ + "if \"IM\" in spectro_Pk.observables:\n", + " print(f\"IM zbins: {spectro_Pk.nuisance.IM_zbins}\")\n", + " print(f\"IM zbins mids: {spectro_Pk.nuisance.IM_zbins_mids}\")\n", + " print(f\"IM bias pars: {spectro_Pk.IMbiaspars}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GCsp zbins: [0.9 1.1 1.3 1.5 1.8]\n", + "GCsp zbins mids: [1. 1.2 1.4 1.65]\n", + "GCsp bias pars: {'lnbg_1': 0.37944989, 'lnbg_2': 0.4738057, 'lnbg_3': 0.55760176, 'lnbg_4': 0.64125687}\n" + ] + } + ], + "source": [ + "if \"GCsp\" in spectro_Pk.observables:\n", + " print(f\"GCsp zbins: {spectro_Pk.nuisance.sp_zbins}\")\n", + " print(f\"GCsp zbins mids: {spectro_Pk.nuisance.sp_zbins_mids}\")\n", + " print(f\"GCsp bias pars: {spectro_Pk.spectrobiaspars}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GC non-linear pars: {}\n", + "GC Pshot pars: {}\n" + ] + } + ], + "source": [ + "print(f\"GC non-linear pars: {spectro_Pk.spectrononlinearpars}\")\n", + "print(f\"GC Pshot pars: {spectro_Pk.PShotpars}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "all_z_bins = np.concatenate([spectro_Pk.nuisance.sp_zbins_mids, spectro_Pk.nuisance.IM_zbins_mids])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GCsp bias term at z=1.00: 1.4614803932771856\n", + "IM bias term at z=1.00: 1.2\n", + "GCsp bias term at z=1.20: 1.606094892470849\n", + "IM bias term at z=1.20: 1.26\n", + "GCsp bias term at z=1.40: 1.7464789980013922\n", + "IM bias term at z=1.40: 1.3199999999999998\n", + "GCsp bias term at z=1.65: 1.89886600781197\n", + "IM bias term at z=1.65: 1.395\n", + "GCsp bias term at z=0.50: 1.4614803932771856\n", + "IM bias term at z=0.50: 1.0499999999999998\n", + "GCsp bias term at z=0.70: 1.4614803932771856\n", + "IM bias term at z=0.70: 1.1099999999999999\n", + "GCsp bias term at z=0.90: 1.4614803932771856\n", + "IM bias term at z=0.90: 1.17\n", + "GCsp bias term at z=1.10: 1.606094892470849\n", + "IM bias term at z=1.10: 1.23\n" + ] + } + ], + "source": [ + "for zz in all_z_bins:\n", + " print(f\"GCsp bias term at z={zz:.2f}: \", spectro_Pk.bterm_fid(zz, bias_sample=\"g\"))\n", + " print(f\"IM bias term at z={zz:.2f}: \", spectro_Pk.bterm_fid(zz, bias_sample=\"I\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compute the observed power spectrum at different redshifts and different angles " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "kk = spectro_Pk.k_grid\n", + "zz= all_z_bins" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.17057226, 0.19456428, 0.21831147, 0.24772251, 0.10955465,\n", + " 0.13409053, 0.15847524, 0.18260125])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectro_Pk.Temperature(zz)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(18, 8))\n", + "\n", + "\n", + "zz1, zz2 = spectro_Pk.nuisance.sp_zbins_mids, spectro_Pk.nuisance.IM_zbins_mids\n", + "\n", + "colormap_z = sns.color_palette(\"autumn\", len(zz1))\n", + "colors_z = iter(colormap_z)\n", + "sm_z = plt.cm.ScalarMappable(cmap=ListedColormap(colormap_z), norm=plt.Normalize(vmin=zz.min(), vmax=zz.max()))\n", + "sm_z.set_array([])\n", + "\n", + "muu = 0.9\n", + "for z1, z2 in zip(zz1, zz1): #at euclid redshifts\n", + " c = next(colors_z)\n", + " axs[0].loglog(kk, spectro_Pk.observed_Pgg(z1, kk, muu), c=c)\n", + " axs[0].loglog(kk, spectro_Pk.observed_P_ij(z2, kk, muu, si='I', sj='I')/(spectro_Pk.Temperature(z2)**2), c=c, ls='--')\n", + " axs[0].loglog(kk, spectro_Pk.observed_P_ij(z2, kk, muu, si='I', sj='g')/(spectro_Pk.Temperature(z2)), c=c, ls=':')\n", + "\n", + "\n", + "axs[0].set_xlabel(r\"$k$ [$\\mathrm{Mpc}^{-1}$]\", fontsize=20)\n", + "axs[0].set_ylabel(r\"$P(k,z,\\mu=1)[\\mathrm{Mpc}^3]$\", fontsize=20)\n", + "axs[0].set_xlim([1e-2, 0.4])\n", + "axs[0].set_ylim([5*1e1, 5*1e5])\n", + "cbar_z = fig.colorbar(sm_z, ax=axs[0])\n", + "cbar_z.set_label('z', fontsize=20, rotation=0)\n", + "\n", + "mus = np.linspace(1, 0, 4)\n", + "colormap_mu = sns.color_palette(\"rocket\", len(mus))\n", + "colors_mu = iter(colormap_mu)\n", + "sm_mu = plt.cm.ScalarMappable(cmap=ListedColormap(colormap_mu), norm=plt.Normalize(vmin=mus.min(), vmax=mus.max()))\n", + "sm_mu.set_array([])\n", + "\n", + "zzii = 1.0\n", + "for mu in mus:\n", + " c = next(colors_mu)\n", + " axs[1].loglog(kk, spectro_Pk.observed_Pgg(zzii, kk, mu), c=c, ls='-')\n", + " axs[1].loglog(kk, spectro_Pk.observed_P_ij(zzii, kk, mu, si='I', sj='I')/(spectro_Pk.Temperature(zzii)**2), c=c, ls='--')\n", + " axs[1].loglog(kk, spectro_Pk.observed_P_ij(zzii, kk, mu, si='I', sj='g')/(spectro_Pk.Temperature(zzii)), c=c, ls=':')\n", + "\n", + "axs[1].set_xlabel(r\"$k$ [$\\mathrm{Mpc}^{-1}$]\", fontsize=20)\n", + "axs[1].set_ylabel(r\"$P(k,z=1,\\mu)[\\mathrm{Mpc}^3]$\", fontsize=20)\n", + "axs[1].set_xlim([1e-2, 0.4])\n", + "axs[1].set_ylim([5*1e1, 5*1e5])\n", + "cbar_mu = fig.colorbar(sm_mu, ax=axs[1])\n", + "cbar_mu.set_label('μ', fontsize=20, rotation=0)\n", + "\n", + "[ax.tick_params(which=\"major\", length=15, width=2, direction=\"in\") for ax in axs]\n", + "[ax.tick_params(which=\"minor\", length=8, width=1, direction=\"in\") for ax in axs]\n", + "[ax.minorticks_on() for ax in axs]\n", + "[ax.tick_params(axis=\"both\", which=\"major\", labelsize=21, pad=10) for ax in axs]\n", + "[ax.tick_params(axis=\"both\", which=\"minor\", labelsize=15) for ax in axs]\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "spectro_Pk.bias_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P_gg at z=1.00: 156148.95289849714\n", + "alt P_gg at z=1.00: 156148.9528984971\n", + "Temperature at z=1.00: 0.17057226296774383\n", + "P_II/T^2 at z=1.00: [122470.36042065]\n", + "P_Ig/T at z=1.00: [138288.17209286]\n", + "---\n", + "P_gg at z=1.20: 151931.80323061428\n", + "alt P_gg at z=1.20: 151931.80323061428\n", + "Temperature at z=1.20: 0.1945642832619074\n", + "P_II/T^2 at z=1.20: [111758.69344862]\n", + "P_Ig/T at z=1.20: [130306.17722252]\n", + "---\n", + "P_gg at z=1.40: 146764.3695549119\n", + "alt P_gg at z=1.40: 146764.36955491186\n", + "Temperature at z=1.40: 0.21831147036593376\n", + "P_II/T^2 at z=1.40: [102009.04082269]\n", + "P_Ig/T at z=1.40: [122357.23339976]\n", + "---\n", + "P_gg at z=1.65: 138102.0875840003\n", + "alt P_gg at z=1.65: 138102.08758400028\n", + "Temperature at z=1.65: 0.247722511709818\n", + "P_II/T^2 at z=1.65: [91257.49530016]\n", + "P_Ig/T at z=1.65: [112262.41850521]\n", + "---\n" + ] + } + ], + "source": [ + "muu = 0.99\n", + "for z1, z2 in zip(zz1, zz2):\n", + " print(f\"P_gg at z={z1:.2f}: {spectro_Pk.observed_Pgg(z1, 0.01, muu)}\")\n", + " print(f\"alt P_gg at z={z1:.2f}: {spectro_Pk.observed_P_ij(z1, 0.01, muu, si='g', sj='g')}\")\n", + " print(f\"Temperature at z={z1:.2f}: {spectro_Pk.Temperature(z1)}\")\n", + " #print(f\"P_HI at z={z1:.2f}: {spectro_Pk.observed_P_HI(z1, 0.01, muu)}\")\n", + " print(f\"P_II/T^2 at z={z1:.2f}: {spectro_Pk.observed_P_ij(z1, 0.01, muu, si='I', sj='I')/(spectro_Pk.Temperature(z1)**2)}\")\n", + " print(f\"P_Ig/T at z={z1:.2f}: {spectro_Pk.observed_P_ij(z1, 0.01, muu, si='I', sj='g')/(spectro_Pk.Temperature(z1))}\")\n", + " print(\"---\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the Power Spectrum from two different cosmologies" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectroscopic zbins mids: [1. 1.2 1.4 1.65]\n", + "Spectroscopic IM zbins mids: [0.5 0.7 0.9 1.1]\n", + "GC inter bins: []\n", + "GC global bins: [0.5 0.7 0.9 1. 1.1 1.2 1.4 1.65]\n" + ] + } + ], + "source": [ + "print(f\"Spectroscopic zbins mids: {spectro_Cov.z_bin_mids}\")\n", + "print(f\"Spectroscopic IM zbins mids: {spectro_Cov.IM_z_bin_mids}\")\n", + "print(f\"GC inter bins: {spectro_Cov.inter_z_bin_mids}\")\n", + "print(f\"GC global bins: {spectro_Cov.global_z_bin_mids}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: MeerKlass-IM-Ze\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-DR1-Pessimistic\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.11 s\n", + "\n", + "In class: FisherMatrix ----> Computing Pk-spectro Fisher matrix\n", + "Computing derivatives of Galaxy Clustering Spectro\n", + ">> Computing Derivs >>\n", + "\n", + " +++ Computing derivative on Omegam\n", + "\n", + "In class: derivatives Derivative on Omegam done! in : 0.28 s\n", + "\n", + " +++ Computing derivative on Omegab\n", + "\n", + "In class: derivatives Derivative on Omegab done! in : 0.27 s\n", + "\n", + " +++ Computing derivative on h\n", + "\n", + "In class: derivatives Derivative on h done! in : 0.30 s\n", + "\n", + " +++ Computing derivative on ns\n", + "\n", + "In class: derivatives Derivative on ns done! in : 0.30 s\n", + "\n", + " +++ Computing derivative on sigma8\n", + "\n", + "In class: derivatives Derivative on sigma8 done! in : 0.32 s\n", + "\n", + " +++ Computing derivative on bI_c1\n", + "\n", + "In class: derivatives Derivative on bI_c1 done! in : 0.06 s\n", + "\n", + " +++ Computing derivative on bI_c2\n", + "\n", + "In class: derivatives Derivative on bI_c2 done! in : 0.06 s\n", + "\n", + "In class: FisherMatrix Fisher Matrix shape: (4, 7, 7)\n", + "\n", + " Fisher matrix calculation finished in 1.68 s\n", + "\n", + " Fisher matrix exported: ./results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_IM_fishermatrix.txt\n", + "\n", + "\n", + "In class: FisherMatrix CosmicFish settings and Survey specifications exported: ./results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_IM_fishermatrix_specifications.dat\n" + ] + } + ], + "source": [ + "observables = [\"IM\"]\n", + "cosmoFM_I = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")\n", + "fish_mat_I = cosmoFM_I.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: MeerKlass-IM-Ze\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-DR1-Pessimistic\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.13 s\n", + "\n", + "In class: FisherMatrix ----> Computing Pk-spectro Fisher matrix\n", + "Computing derivatives of Galaxy Clustering Spectro\n", + ">> Computing Derivs >>\n", + "\n", + " +++ Computing derivative on Omegam\n", + "\n", + "In class: derivatives Derivative on Omegam done! in : 0.22 s\n", + "\n", + " +++ Computing derivative on Omegab\n", + "\n", + "In class: derivatives Derivative on Omegab done! in : 0.21 s\n", + "\n", + " +++ Computing derivative on h\n", + "\n", + "In class: derivatives Derivative on h done! in : 0.22 s\n", + "\n", + " +++ Computing derivative on ns\n", + "\n", + "In class: derivatives Derivative on ns done! in : 0.22 s\n", + "\n", + " +++ Computing derivative on sigma8\n", + "\n", + "In class: derivatives Derivative on sigma8 done! in : 0.22 s\n", + "\n", + " +++ Computing derivative on lnbg_1\n", + "\n", + "In class: derivatives Derivative on lnbg_1 done! in : 0.00 s\n", + "\n", + " +++ Computing derivative on lnbg_2\n", + "\n", + "In class: derivatives Derivative on lnbg_2 done! in : 0.00 s\n", + "\n", + " +++ Computing derivative on lnbg_3\n", + "\n", + "In class: derivatives Derivative on lnbg_3 done! in : 0.00 s\n", + "\n", + " +++ Computing derivative on lnbg_4\n", + "\n", + "In class: derivatives Derivative on lnbg_4 done! in : 0.00 s\n", + "\n", + " +++ Computing derivative on bI_c1\n", + "\n", + "In class: derivatives Derivative on bI_c1 done! in : 0.00 s\n", + "\n", + " +++ Computing derivative on bI_c2\n", + "\n", + "In class: derivatives Derivative on bI_c2 done! in : 0.00 s\n", + "\n", + "In class: FisherMatrix Fisher Matrix shape: (0, 11, 11)\n", + "\n", + " Fisher matrix calculation finished in 1.09 s\n", + "\n", + " Fisher matrix exported: ./results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCspIM_fishermatrix.txt\n", + "Columns and Rows with zeros will be deleted\n", + "\n", + "\n", + "In class: FisherMatrix CosmicFish settings and Survey specifications exported: ./results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCspIM_fishermatrix_specifications.dat\n" + ] + } + ], + "source": [ + "observables = [\"GCsp\", \"IM\"]\n", + "cosmoFM_Ig = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")\n", + "fish_mat_Ig = cosmoFM_Ig.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "spectro_Pk_Ig = spobs.ComputeGalSpectro(cosmoFM_Ig.fiducialcosmopars, \n", + " configuration=cosmoFM_Ig)\n", + "spectro_Cov_Ig = spcov.SpectroCov(cosmoFM_Ig.fiducialcosmopars, \n", + " fiducial_specobs=spectro_Pk_Ig,\n", + " configuration=cosmoFM_Ig)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: MeerKlass-IM-Ze\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-DR1-Pessimistic\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.11 s\n", + "\n", + "In class: FisherMatrix ----> Computing Pk-spectro Fisher matrix\n", + "Computing derivatives of Galaxy Clustering Spectro\n", + ">> Computing Derivs >>\n", + "\n", + " +++ Computing derivative on Omegam\n", + "\n", + "In class: derivatives Derivative on Omegam done! in : 0.27 s\n", + "\n", + " +++ Computing derivative on Omegab\n", + "\n", + "In class: derivatives Derivative on Omegab done! in : 0.27 s\n", + "\n", + " +++ Computing derivative on h\n", + "\n", + "In class: derivatives Derivative on h done! in : 0.27 s\n", + "\n", + " +++ Computing derivative on ns\n", + "\n", + "In class: derivatives Derivative on ns done! in : 0.26 s\n", + "\n", + " +++ Computing derivative on sigma8\n", + "\n", + "In class: derivatives Derivative on sigma8 done! in : 0.27 s\n", + "\n", + " +++ Computing derivative on lnbg_1\n", + "\n", + "In class: derivatives Derivative on lnbg_1 done! in : 0.05 s\n", + "\n", + " +++ Computing derivative on lnbg_2\n", + "\n", + "In class: derivatives Derivative on lnbg_2 done! in : 0.05 s\n", + "\n", + " +++ Computing derivative on lnbg_3\n", + "\n", + "In class: derivatives Derivative on lnbg_3 done! in : 0.05 s\n", + "\n", + " +++ Computing derivative on lnbg_4\n", + "\n", + "In class: derivatives Derivative on lnbg_4 done! in : 0.05 s\n", + "\n", + "In class: FisherMatrix Fisher Matrix shape: (4, 9, 9)\n", + "\n", + " Fisher matrix calculation finished in 1.60 s\n", + "\n", + " Fisher matrix exported: ./results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCsp_fishermatrix.txt\n", + "\n", + "\n", + "In class: FisherMatrix CosmicFish settings and Survey specifications exported: ./results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCsp_fishermatrix_specifications.dat\n" + ] + } + ], + "source": [ + "observables = [\"GCsp\"]\n", + "cosmoFM_g = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")\n", + "fish_mat_g = cosmoFM_g.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "params_Ig = fish_mat_Ig.get_param_names()\n", + "print(params_Ig)\n", + "params_Ig_tex = fish_mat_Ig.get_param_names_latex()\n", + "#print(params_Ig_tex)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Omegam', 'Omegab', 'h', 'ns', 'sigma8', 'bI_c1', 'bI_c2']\n", + "['Omegam', 'Omegab', 'h', 'ns', 'sigma8', 'lnbg_1', 'lnbg_2', 'lnbg_3', 'lnbg_4']\n" + ] + } + ], + "source": [ + "params_I = fish_mat_I.get_param_names()\n", + "print(params_I)\n", + "params_I_tex = fish_mat_I.get_param_names_latex()\n", + "#print(params_I_tex)\n", + "params_g = fish_mat_g.get_param_names()\n", + "print(params_g)\n", + "params_g_tex = fish_mat_g.get_param_names_latex()\n", + "#print(params_g_tex)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fish_mat_Ig shape: (0, 0)\n", + "fish_mat_I shape: (7, 7)\n", + "fish_mat_g shape: (9, 9)\n" + ] + } + ], + "source": [ + "print(f\"fish_mat_Ig shape: {fish_mat_Ig.get_fisher_matrix().shape}\")\n", + "print(f\"fish_mat_I shape: {fish_mat_I.get_fisher_matrix().shape}\")\n", + "print(f\"fish_mat_g shape: {fish_mat_g.get_fisher_matrix().shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from cosmicfishpie.analysis.fishconsumer import simple_fisher_plot\n", + "from cosmicfishpie.analysis.fishconsumer import fishtable_to_pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Omegam',\n", + " 'Omegab',\n", + " 'h',\n", + " 'ns',\n", + " 'sigma8',\n", + " 'bI_c1',\n", + " 'bI_c2',\n", + " 'lnbg_1',\n", + " 'lnbg_2',\n", + " 'lnbg_3',\n", + " 'lnbg_4']" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fish_mat_all = fish_mat_Ig + fish_mat_I + fish_mat_g\n", + "fish_mat_all.get_param_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example usage\n", + "#fisher_matrices = [fish_mat_Ig, fish_mat_I, fish_mat_g, fish_mat_all] # Your Fisher matrix objects\n", + "fisher_matrices = [fish_mat_I, fish_mat_g, fish_mat_all] # Your Fisher matrix objects\n", + "params = params_I[:5] # Parameters to plot\n", + "#labels = ['IMxGCsp', 'SKAO IM', 'Euclid GCsp', 'IMxGCsp+IM+GCsp'] # Optional labels\n", + "labels = ['MeerKlass IM', 'Euclid DR1 GCsp', 'IMxGCsp+IM+GCsp'] # Optional labels\n", + "#colors = ['orange', 'blue', 'green', 'red'] # Optional colors\n", + "colors = ['blue', 'green', 'red'] # Optional colors\n", + "\n", + "fig = simple_fisher_plot(\n", + " fisher_matrices,\n", + " params,\n", + " labels=labels,\n", + " colors=colors,\n", + " n_samples=10000,\n", + " legend=True,\n", + " #save_plot=True,\n", + " output_file='my_fisher_plot.pdf'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cosmicfishpie.analysis.fisher_plot_analysis as fpa\n", + "import cosmicfishpie.analysis.fisher_matrix as fma\n", + "cfanaly = fpa.CosmicFish_FisherAnalysis(fisher_list=[fish_mat_I, fish_mat_g, fish_mat_all])\n", + "fishlist = cfanaly.get_fisher_list()\n", + "fishlist" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Omegam', 'Omegab', 'h', 'ns', 'sigma8']" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "pd = fishtable_to_pandas(params, cfanaly)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----\n", + "Old Fisher Name: CosmicFish_v1.2.0_GCsp_DR1+Meerklass_IM_fishermatrix\n", + "New Fisher Name: SKAO AA4 IM\n", + "Parameter Omegam, fiducial: 0.320, 1-sigma error: 0.0272, percent error: 8.5%\n", + "Parameter Omegab, fiducial: 0.050, 1-sigma error: 0.0101, percent error: 20.1%\n", + "Parameter h, fiducial: 0.670, 1-sigma error: 0.0832, percent error: 12.4%\n", + "Parameter ns, fiducial: 0.960, 1-sigma error: 0.0488, percent error: 5.1%\n", + "Parameter sigma8, fiducial: 0.816, 1-sigma error: 0.0302, percent error: 3.7%\n", + "Parameter bI_c1, fiducial: 0.300, 1-sigma error: 0.0390, percent error: 13.0%\n", + "Parameter bI_c2, fiducial: 0.600, 1-sigma error: 0.0501, percent error: 8.4%\n", + "----\n", + "Old Fisher Name: CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCsp_fishermatrix\n", + "New Fisher Name: Euclid DR3 GCsp\n", + "Parameter Omegam, fiducial: 0.320, 1-sigma error: 0.0170, percent error: 5.3%\n", + "Parameter Omegab, fiducial: 0.050, 1-sigma error: 0.0061, percent error: 12.1%\n", + "Parameter h, fiducial: 0.670, 1-sigma error: 0.0564, percent error: 8.4%\n", + "Parameter ns, fiducial: 0.960, 1-sigma error: 0.0336, percent error: 3.5%\n", + "Parameter sigma8, fiducial: 0.816, 1-sigma error: 0.0212, percent error: 2.6%\n", + "Parameter lnbg_1, fiducial: 0.379, 1-sigma error: 0.0276, percent error: 7.3%\n", + "Parameter lnbg_2, fiducial: 0.474, 1-sigma error: 0.0277, percent error: 5.8%\n", + "Parameter lnbg_3, fiducial: 0.558, 1-sigma error: 0.0278, percent error: 5.0%\n", + "Parameter lnbg_4, fiducial: 0.641, 1-sigma error: 0.0280, percent error: 4.4%\n", + "----\n", + "Old Fisher Name: CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCspIM_fishermatrix_CosmicFish_v1.2.0_GCsp_DR1+Meerklass_IM_fishermatrix_CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCsp_fishermatrix\n", + "New Fisher Name: IMxGCsp+IM+GCsp\n", + "Parameter Omegam, fiducial: 0.320, 1-sigma error: 0.0132, percent error: 4.1%\n", + "Parameter Omegab, fiducial: 0.050, 1-sigma error: 0.0049, percent error: 9.8%\n", + "Parameter h, fiducial: 0.670, 1-sigma error: 0.0449, percent error: 6.7%\n", + "Parameter ns, fiducial: 0.960, 1-sigma error: 0.0263, percent error: 2.7%\n", + "Parameter sigma8, fiducial: 0.816, 1-sigma error: 0.0165, percent error: 2.0%\n", + "Parameter bI_c1, fiducial: 0.300, 1-sigma error: 0.0288, percent error: 9.6%\n", + "Parameter bI_c2, fiducial: 0.600, 1-sigma error: 0.0455, percent error: 7.6%\n", + "Parameter lnbg_1, fiducial: 0.379, 1-sigma error: 0.0211, percent error: 5.6%\n", + "Parameter lnbg_2, fiducial: 0.474, 1-sigma error: 0.0212, percent error: 4.5%\n", + "Parameter lnbg_3, fiducial: 0.558, 1-sigma error: 0.0213, percent error: 3.8%\n", + "Parameter lnbg_4, fiducial: 0.641, 1-sigma error: 0.0214, percent error: 3.3%\n" + ] + } + ], + "source": [ + "for ii, fish in enumerate(fishlist):\n", + " print(\"----\")\n", + " print(\"Old Fisher Name: \", fish.name)\n", + " fish.name = labels[ii]\n", + " print(\"New Fisher Name: \", fish.name)\n", + " sigmas = fish.get_confidence_bounds()\n", + " fidus = fish.get_param_fiducial()\n", + " parnames = fish.get_param_names()\n", + " #fiww = fo.marginalise(fish, parstomarg)\n", + " #deFoM = np.sqrt(fiww.determinant())\n", + " #print(\"Fisher FoM: \", deFoM)\n", + " for ii, par in enumerate(parnames):\n", + " print(\"Parameter {:s}, fiducial: {:.3f}, 1-sigma error: {:.4f}, percent error: {:.1f}%\".format(\n", + " par, fidus[ii], abs(sigmas[ii]), abs(100*sigmas[ii]/fidus[ii])))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['./results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCspIM_fishermatrix.txt',\n", + " './results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_GCsp_fishermatrix.txt',\n", + " './results/CosmicFish_v1.2.0_GCsp_DR1+Meerklass_IM_fishermatrix.txt',\n", + " './results/CosmicFish_v1.2.0_GCsp_presentation_GCspIM_fishermatrix.txt',\n", + " './results/CosmicFish_v1.2.0_GCsp_presentation_GCsp_fishermatrix.txt',\n", + " './results/CosmicFish_v1.2.0_GCsp_presentation_IM_fishermatrix.txt']" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import glob\n", + "files = glob.glob(\"./results/*.txt\")\n", + "files.sort()\n", + "files" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns and Rows with zeros will be deleted\n" + ] + } + ], + "source": [ + "fishmat_DR3_GC = fma.fisher_matrix(file_name=files[-2])\n", + "fishmat_AA4_IM = fma.fisher_matrix(file_name=files[-1])\n", + "fishmat_DR3AA4_IMGC = fma.fisher_matrix(file_name=files[-3])\n", + "fishmat_DR1_GC = fma.fisher_matrix(file_name=files[1])\n", + "fishmat_MK_IM = fma.fisher_matrix(file_name=files[2])\n", + "fishmat_DR1MK_IMGC = fma.fisher_matrix(file_name=files[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "fishmat_DR3AA4_all = fishmat_DR3AA4_IMGC + fishmat_DR3_GC + fishmat_AA4_IM\n", + "fishmat_DR1MK_all = fishmat_DR1MK_IMGC + fishmat_DR1_GC + fishmat_MK_IM\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example usage\n", + "#fisher_matrices = [fish_mat_Ig, fish_mat_I, fish_mat_g, fish_mat_all] # Your Fisher matrix objects\n", + "fisher_matrices = [fishmat_DR3AA4_all, fishmat_DR1MK_all] # Your Fisher matrix objects\n", + "params = params_I[:5] # Parameters to plot\n", + "#labels = ['IMxGCsp', 'SKAO IM', 'Euclid GCsp', 'IMxGCsp+IM+GCsp'] # Optional labels\n", + "labels = ['Euclid DR3 * SKAO', 'Euclid DR1 * MeerKlass'] # Optional labels\n", + "#colors = ['orange', 'blue', 'green', 'red'] # Optional colors\n", + "colors = ['blue', 'green', 'red'] # Optional colors\n", + "\n", + "fig = simple_fisher_plot(\n", + " fisher_matrices,\n", + " params,\n", + " labels=labels,\n", + " colors=colors,\n", + " n_samples=10000,\n", + " legend=True,\n", + " #save_plot=True,\n", + " output_file='my_fisher_plot.pdf'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example usage\n", + "#fisher_matrices = [fish_mat_Ig, fish_mat_I, fish_mat_g, fish_mat_all] # Your Fisher matrix objects\n", + "fisher_matrices = [fishmat_MK_IM, fishmat_DR1_GC] # Your Fisher matrix objects\n", + "params = params_g[:5] # Parameters to plot\n", + "#labels = ['IMxGCsp', 'SKAO IM', 'Euclid GCsp', 'IMxGCsp+IM+GCsp'] # Optional labels\n", + "labels = ['MeerKlass IM', 'Euclid DR1'] # Optional labels\n", + "colors = ['orange', 'purple'] # Optional colors\n", + "#colors = ['blue', 'green', 'red'] # Optional colors\n", + "\n", + "fig = simple_fisher_plot(\n", + " fisher_matrices,\n", + " params,\n", + " labels=labels,\n", + " colors=colors,\n", + " n_samples=10000,\n", + " legend=True,\n", + " #save_plot=True,\n", + " output_file='my_fisher_plot.pdf'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cosmicfish", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/SKAO_presentation.ipynb b/notebooks/SKAO_presentation.ipynb new file mode 100644 index 0000000..2f590a3 --- /dev/null +++ b/notebooks/SKAO_presentation.ipynb @@ -0,0 +1,1186 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import ListedColormap\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "snscolors = sns.color_palette(\"colorblind\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from cosmicfishpie.fishermatrix import cosmicfish\n", + "from cosmicfishpie.utilities.utils import printing as upt\n", + "upt.debug = False\n", + "upt.debug_print(\"Debug is on\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "fiducial = {\n", + " \"Omegam\": 0.32,\n", + " \"Omegab\": 0.05,\n", + " \"h\": 0.67,\n", + " \"ns\": 0.96,\n", + " \"sigma8\": 0.815584,\n", + " \"w0\": -1.0,\n", + " \"wa\": 0.0,\n", + " \"mnu\": 0.06,\n", + " \"Neff\": 3.044,\n", + "}\n", + "\n", + "options = {\n", + " \"accuracy\": 1,\n", + " \"feedback\": 1,\n", + " \"code\": \"symbolic\",\n", + " \"outroot\": \"GCsp_presentation\",\n", + " \"survey_name\": \"SKAO\",\n", + " #\"survey_name_spectro\": \"SKAO-Spectroscopic-Redbook\",\n", + " \"survey_name_spectro\": \"Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv\",\n", + " \"survey_name_photo\": False,\n", + " \"survey_name_radio_IM\": \"SKAO-IM-Redbook\",\n", + " 'specs_dir': '../cosmicfishpie/configs/other_survey_specifications/',\n", + " \"cosmo_model\": \"LCDM\",\n", + " \"bfs8terms\": False,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: SKAO-IM-Redbook\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.11 s\n" + ] + } + ], + "source": [ + "observables = [\"GCsp\", \"IM\"]\n", + "cosmoFM_A = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spectroscopic Power Spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Omegam': 0.01,\n", + " 'Omegab': 0.01,\n", + " 'h': 0.01,\n", + " 'ns': 0.01,\n", + " 'sigma8': 0.01,\n", + " 'lnbg_1': 0.0001,\n", + " 'lnbg_2': 0.0001,\n", + " 'lnbg_3': 0.0001,\n", + " 'lnbg_4': 0.0001,\n", + " 'bI_c1': 0.0001,\n", + " 'bI_c2': 0.0001}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cosmoFM_A.freeparams" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from cosmicfishpie.LSSsurvey import spectro_obs as spobs\n", + "from cosmicfishpie.LSSsurvey import spectro_cov as spcov\n", + "\n", + "spectro_Pk = spobs.ComputeGalSpectro(cosmoFM_A.fiducialcosmopars)\n", + "spectro_Cov = spcov.SpectroCov(cosmoFM_A.fiducialcosmopars, \n", + " fiducial_specobs=spectro_Pk)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['GCsp', 'IM']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectro_Pk.observables" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IM zbins: [0.5 0.7 0.9 1.1 1.3 1.5 1.8 2. 2.2 2.4 2.6]\n", + "IM zbins mids: [0.6 0.8 1. 1.2 1.4 1.65 1.9 2.1 2.3 2.5 ]\n", + "IM bias pars: {'bI_c1': 0.3, 'bI_c2': 0.6}\n" + ] + } + ], + "source": [ + "if \"IM\" in spectro_Pk.observables:\n", + " print(f\"IM zbins: {spectro_Pk.nuisance.IM_zbins}\")\n", + " print(f\"IM zbins mids: {spectro_Pk.nuisance.IM_zbins_mids}\")\n", + " print(f\"IM bias pars: {spectro_Pk.IMbiaspars}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GCsp zbins: [0.9 1.1 1.3 1.5 1.8]\n", + "GCsp zbins mids: [1. 1.2 1.4 1.65]\n", + "GCsp bias pars: {'lnbg_1': 0.37944989, 'lnbg_2': 0.4738057, 'lnbg_3': 0.55760176, 'lnbg_4': 0.64125687}\n" + ] + } + ], + "source": [ + "if \"GCsp\" in spectro_Pk.observables:\n", + " print(f\"GCsp zbins: {spectro_Pk.nuisance.sp_zbins}\")\n", + " print(f\"GCsp zbins mids: {spectro_Pk.nuisance.sp_zbins_mids}\")\n", + " print(f\"GCsp bias pars: {spectro_Pk.spectrobiaspars}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GC non-linear pars: {}\n", + "GC Pshot pars: {}\n" + ] + } + ], + "source": [ + "print(f\"GC non-linear pars: {spectro_Pk.spectrononlinearpars}\")\n", + "print(f\"GC Pshot pars: {spectro_Pk.PShotpars}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "all_z_bins = np.concatenate([spectro_Pk.nuisance.sp_zbins_mids, spectro_Pk.nuisance.IM_zbins_mids])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GCsp bias term at z=1.00: 1.4614803932771856\n", + "IM bias term at z=1.00: 1.2\n", + "GCsp bias term at z=1.20: 1.606094892470849\n", + "IM bias term at z=1.20: 1.26\n", + "GCsp bias term at z=1.40: 1.7464789980013922\n", + "IM bias term at z=1.40: 1.3199999999999998\n", + "GCsp bias term at z=1.65: 1.89886600781197\n", + "IM bias term at z=1.65: 1.395\n", + "GCsp bias term at z=0.60: 1.4614803932771856\n", + "IM bias term at z=0.60: 1.08\n", + "GCsp bias term at z=0.80: 1.4614803932771856\n", + "IM bias term at z=0.80: 1.1400000000000001\n", + "GCsp bias term at z=1.00: 1.4614803932771856\n", + "IM bias term at z=1.00: 1.2\n", + "GCsp bias term at z=1.20: 1.606094892470849\n", + "IM bias term at z=1.20: 1.26\n", + "GCsp bias term at z=1.40: 1.7464789980013922\n", + "IM bias term at z=1.40: 1.3199999999999998\n", + "GCsp bias term at z=1.65: 1.89886600781197\n", + "IM bias term at z=1.65: 1.395\n", + "GCsp bias term at z=1.90: 1.89886600781197\n", + "IM bias term at z=1.90: 1.47\n", + "GCsp bias term at z=2.10: 1.89886600781197\n", + "IM bias term at z=2.10: 1.5299999999999998\n", + "GCsp bias term at z=2.30: 1.89886600781197\n", + "IM bias term at z=2.30: 1.5899999999999999\n", + "GCsp bias term at z=2.50: 1.89886600781197\n", + "IM bias term at z=2.50: 1.65\n" + ] + } + ], + "source": [ + "for zz in all_z_bins:\n", + " print(f\"GCsp bias term at z={zz:.2f}: \", spectro_Pk.bterm_fid(zz, bias_sample=\"g\"))\n", + " print(f\"IM bias term at z={zz:.2f}: \", spectro_Pk.bterm_fid(zz, bias_sample=\"I\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compute the observed power spectrum at different redshifts and different angles " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "kk = spectro_Pk.k_grid\n", + "zz= all_z_bins" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.17057226, 0.19456428, 0.21831147, 0.24772251, 0.12182797,\n", + " 0.14631201, 0.17057226, 0.19456428, 0.21831147, 0.24772251,\n", + " 0.27691885, 0.30017661, 0.32338268, 0.34656355])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectro_Pk.Temperature(zz)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(18, 8))\n", + "\n", + "\n", + "zz1, zz2 = spectro_Pk.nuisance.sp_zbins_mids, spectro_Pk.nuisance.IM_zbins_mids\n", + "\n", + "colormap_z = sns.color_palette(\"autumn\", len(zz1))\n", + "colors_z = iter(colormap_z)\n", + "sm_z = plt.cm.ScalarMappable(cmap=ListedColormap(colormap_z), norm=plt.Normalize(vmin=zz.min(), vmax=zz.max()))\n", + "sm_z.set_array([])\n", + "\n", + "muu = 0.9\n", + "for z1, z2 in zip(zz1, zz1): #at euclid redshifts\n", + " c = next(colors_z)\n", + " axs[0].loglog(kk, spectro_Pk.observed_Pgg(z1, kk, muu), c=c)\n", + " axs[0].loglog(kk, spectro_Pk.observed_P_ij(z2, kk, muu, si='I', sj='I')/(spectro_Pk.Temperature(z2)**2), c=c, ls='--')\n", + " axs[0].loglog(kk, spectro_Pk.observed_P_ij(z2, kk, muu, si='I', sj='g')/(spectro_Pk.Temperature(z2)), c=c, ls=':')\n", + "\n", + "\n", + "axs[0].set_xlabel(r\"$k$ [$\\mathrm{Mpc}^{-1}$]\", fontsize=20)\n", + "axs[0].set_ylabel(r\"$P(k,z,\\mu=1)[\\mathrm{Mpc}^3]$\", fontsize=20)\n", + "axs[0].set_xlim([1e-2, 0.4])\n", + "axs[0].set_ylim([5*1e1, 5*1e5])\n", + "cbar_z = fig.colorbar(sm_z, ax=axs[0])\n", + "cbar_z.set_label('z', fontsize=20, rotation=0)\n", + "\n", + "mus = np.linspace(1, 0, 4)\n", + "colormap_mu = sns.color_palette(\"rocket\", len(mus))\n", + "colors_mu = iter(colormap_mu)\n", + "sm_mu = plt.cm.ScalarMappable(cmap=ListedColormap(colormap_mu), norm=plt.Normalize(vmin=mus.min(), vmax=mus.max()))\n", + "sm_mu.set_array([])\n", + "\n", + "zzii = 1.0\n", + "for mu in mus:\n", + " c = next(colors_mu)\n", + " axs[1].loglog(kk, spectro_Pk.observed_Pgg(zzii, kk, mu), c=c, ls='-')\n", + " axs[1].loglog(kk, spectro_Pk.observed_P_ij(zzii, kk, mu, si='I', sj='I')/(spectro_Pk.Temperature(zzii)**2), c=c, ls='--')\n", + " axs[1].loglog(kk, spectro_Pk.observed_P_ij(zzii, kk, mu, si='I', sj='g')/(spectro_Pk.Temperature(zzii)), c=c, ls=':')\n", + "\n", + "axs[1].set_xlabel(r\"$k$ [$\\mathrm{Mpc}^{-1}$]\", fontsize=20)\n", + "axs[1].set_ylabel(r\"$P(k,z=1,\\mu)[\\mathrm{Mpc}^3]$\", fontsize=20)\n", + "axs[1].set_xlim([1e-2, 0.4])\n", + "axs[1].set_ylim([5*1e1, 5*1e5])\n", + "cbar_mu = fig.colorbar(sm_mu, ax=axs[1])\n", + "cbar_mu.set_label('μ', fontsize=20, rotation=0)\n", + "\n", + "[ax.tick_params(which=\"major\", length=15, width=2, direction=\"in\") for ax in axs]\n", + "[ax.tick_params(which=\"minor\", length=8, width=1, direction=\"in\") for ax in axs]\n", + "[ax.minorticks_on() for ax in axs]\n", + "[ax.tick_params(axis=\"both\", which=\"major\", labelsize=21, pad=10) for ax in axs]\n", + "[ax.tick_params(axis=\"both\", which=\"minor\", labelsize=15) for ax in axs]\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "spectro_Pk.bias_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P_gg at z=1.00: 156148.95289849714\n", + "alt P_gg at z=1.00: 156148.9528984971\n", + "Temperature at z=1.00: 0.17057226296774383\n", + "P_II/T^2 at z=1.00: [122668.29544108]\n", + "P_Ig/T at z=1.00: [138399.87675922]\n", + "---\n", + "P_gg at z=1.20: 151931.80323061428\n", + "alt P_gg at z=1.20: 151931.80323061428\n", + "Temperature at z=1.20: 0.1945642832619074\n", + "P_II/T^2 at z=1.20: [112041.61227681]\n", + "P_Ig/T at z=1.20: [130471.00900231]\n", + "---\n", + "P_gg at z=1.40: 146764.3695549119\n", + "alt P_gg at z=1.40: 146764.36955491186\n", + "Temperature at z=1.40: 0.21831147036593376\n", + "P_II/T^2 at z=1.40: [102386.47426379]\n", + "P_Ig/T at z=1.40: [122583.38527825]\n", + "---\n", + "P_gg at z=1.65: 138102.0875840003\n", + "alt P_gg at z=1.65: 138102.08758400028\n", + "Temperature at z=1.65: 0.247722511709818\n", + "P_II/T^2 at z=1.65: [91763.18536968]\n", + "P_Ig/T at z=1.65: [112573.0316857]\n", + "---\n" + ] + } + ], + "source": [ + "muu = 0.99\n", + "for z1, z2 in zip(zz1, zz2):\n", + " print(f\"P_gg at z={z1:.2f}: {spectro_Pk.observed_Pgg(z1, 0.01, muu)}\")\n", + " print(f\"alt P_gg at z={z1:.2f}: {spectro_Pk.observed_P_ij(z1, 0.01, muu, si='g', sj='g')}\")\n", + " print(f\"Temperature at z={z1:.2f}: {spectro_Pk.Temperature(z1)}\")\n", + " #print(f\"P_HI at z={z1:.2f}: {spectro_Pk.observed_P_HI(z1, 0.01, muu)}\")\n", + " print(f\"P_II/T^2 at z={z1:.2f}: {spectro_Pk.observed_P_ij(z1, 0.01, muu, si='I', sj='I')/(spectro_Pk.Temperature(z1)**2)}\")\n", + " print(f\"P_Ig/T at z={z1:.2f}: {spectro_Pk.observed_P_ij(z1, 0.01, muu, si='I', sj='g')/(spectro_Pk.Temperature(z1))}\")\n", + " print(\"---\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the Power Spectrum from two different cosmologies" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectroscopic zbins mids: [1. 1.2 1.4 1.65]\n", + "Spectroscopic IM zbins mids: [0.6 0.8 1. 1.2 1.4 1.65 1.9 2.1 2.3 2.5 ]\n", + "GC inter bins: [1. 1.2 1.4 1.65]\n", + "GC global bins: [0.6 0.8 1. 1.2 1.4 1.65 1.9 2.1 2.3 2.5 ]\n" + ] + } + ], + "source": [ + "print(f\"Spectroscopic zbins mids: {spectro_Cov.z_bin_mids}\")\n", + "print(f\"Spectroscopic IM zbins mids: {spectro_Cov.IM_z_bin_mids}\")\n", + "print(f\"GC inter bins: {spectro_Cov.inter_z_bin_mids}\")\n", + "print(f\"GC global bins: {spectro_Cov.global_z_bin_mids}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: SKAO-IM-Redbook\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.11 s\n", + "\n", + "In class: FisherMatrix ----> Computing Pk-spectro Fisher matrix\n", + "Computing derivatives of Galaxy Clustering Spectro\n", + ">> Computing Derivs >>\n", + "\n", + " +++ Computing derivative on Omegam\n", + "\n", + "In class: derivatives Derivative on Omegam done! in : 0.39 s\n", + "\n", + " +++ Computing derivative on Omegab\n", + "\n", + "In class: derivatives Derivative on Omegab done! in : 0.39 s\n", + "\n", + " +++ Computing derivative on h\n", + "\n", + "In class: derivatives Derivative on h done! in : 0.39 s\n", + "\n", + " +++ Computing derivative on ns\n", + "\n", + "In class: derivatives Derivative on ns done! in : 0.39 s\n", + "\n", + " +++ Computing derivative on sigma8\n", + "\n", + "In class: derivatives Derivative on sigma8 done! in : 0.39 s\n", + "\n", + " +++ Computing derivative on bI_c1\n", + "\n", + "In class: derivatives Derivative on bI_c1 done! in : 0.16 s\n", + "\n", + " +++ Computing derivative on bI_c2\n", + "\n", + "In class: derivatives Derivative on bI_c2 done! in : 0.16 s\n", + "\n", + "In class: FisherMatrix Fisher Matrix shape: (10, 7, 7)\n", + "\n", + " Fisher matrix calculation finished in 2.51 s\n", + "\n", + " Fisher matrix exported: ./results/CosmicFish_v1.2.0_GCsp_presentation_IM_fishermatrix.txt\n", + "\n", + "\n", + "In class: FisherMatrix CosmicFish settings and Survey specifications exported: ./results/CosmicFish_v1.2.0_GCsp_presentation_IM_fishermatrix_specifications.dat\n" + ] + } + ], + "source": [ + "observables = [\"IM\"]\n", + "cosmoFM_I = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")\n", + "fish_mat_I = cosmoFM_I.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: SKAO-IM-Redbook\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.14 s\n", + "\n", + "In class: FisherMatrix ----> Computing Pk-spectro Fisher matrix\n", + "Entering veff_XC term\n", + "Entering veff_XC term\n", + "Entering veff_XC term\n", + "Entering veff_XC term\n", + "Computing derivatives of Galaxy Clustering Spectro\n", + ">> Computing Derivs >>\n", + "\n", + " +++ Computing derivative on Omegam\n", + "\n", + "In class: derivatives Derivative on Omegam done! in : 0.34 s\n", + "\n", + " +++ Computing derivative on Omegab\n", + "\n", + "In class: derivatives Derivative on Omegab done! in : 0.29 s\n", + "\n", + " +++ Computing derivative on h\n", + "\n", + "In class: derivatives Derivative on h done! in : 0.31 s\n", + "\n", + " +++ Computing derivative on ns\n", + "\n", + "In class: derivatives Derivative on ns done! in : 0.33 s\n", + "\n", + " +++ Computing derivative on sigma8\n", + "\n", + "In class: derivatives Derivative on sigma8 done! in : 0.29 s\n", + "\n", + " +++ Computing derivative on lnbg_1\n", + "\n", + "In class: derivatives Derivative on lnbg_1 done! in : 0.07 s\n", + "\n", + " +++ Computing derivative on lnbg_2\n", + "\n", + "In class: derivatives Derivative on lnbg_2 done! in : 0.06 s\n", + "\n", + " +++ Computing derivative on lnbg_3\n", + "\n", + "In class: derivatives Derivative on lnbg_3 done! in : 0.07 s\n", + "\n", + " +++ Computing derivative on lnbg_4\n", + "\n", + "In class: derivatives Derivative on lnbg_4 done! in : 0.06 s\n", + "\n", + " +++ Computing derivative on bI_c1\n", + "\n", + "In class: derivatives Derivative on bI_c1 done! in : 0.07 s\n", + "\n", + " +++ Computing derivative on bI_c2\n", + "\n", + "In class: derivatives Derivative on bI_c2 done! in : 0.06 s\n", + "\n", + "In class: FisherMatrix Fisher Matrix shape: (4, 11, 11)\n", + "\n", + " Fisher matrix calculation finished in 2.15 s\n", + "\n", + " Fisher matrix exported: ./results/CosmicFish_v1.2.0_GCsp_presentation_GCspIM_fishermatrix.txt\n", + "\n", + "\n", + "In class: FisherMatrix CosmicFish settings and Survey specifications exported: ./results/CosmicFish_v1.2.0_GCsp_presentation_GCspIM_fishermatrix_specifications.dat\n" + ] + } + ], + "source": [ + "observables = [\"GCsp\", \"IM\"]\n", + "cosmoFM_Ig = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")\n", + "fish_mat_Ig = cosmoFM_Ig.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "spectro_Pk_Ig = spobs.ComputeGalSpectro(cosmoFM_Ig.fiducialcosmopars, \n", + " configuration=cosmoFM_Ig)\n", + "spectro_Cov_Ig = spcov.SpectroCov(cosmoFM_Ig.fiducialcosmopars, \n", + " fiducial_specobs=spectro_Pk_Ig,\n", + " configuration=cosmoFM_Ig)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****************************************************************\n", + " _____ _ _____ __ \n", + " / ___/__ ___ __ _ (_)___/ __(_)__ / / \n", + " / /__/ _ \\(_- Survey loaded: SKAO-IM-Redbook\n", + "\n", + " -> Survey loaded: Euclid-Spectroscopic-ISTF-Pessimistic-sigma_pv\n", + "\n", + " -> No photo survey passed, returning empty dict\n", + "\n", + " -> Survey loaded: False\n", + "\n", + " -> Computing cosmology at the fiducial point\n", + "\n", + " ---> Cosmological functions obtained in: 0.14 s\n", + "\n", + "In class: FisherMatrix ----> Computing Pk-spectro Fisher matrix\n", + "Computing derivatives of Galaxy Clustering Spectro\n", + ">> Computing Derivs >>\n", + "\n", + " +++ Computing derivative on Omegam\n", + "\n", + "In class: derivatives Derivative on Omegam done! in : 0.29 s\n", + "\n", + " +++ Computing derivative on Omegab\n", + "\n", + "In class: derivatives Derivative on Omegab done! in : 0.29 s\n", + "\n", + " +++ Computing derivative on h\n", + "\n", + "In class: derivatives Derivative on h done! in : 0.29 s\n", + "\n", + " +++ Computing derivative on ns\n", + "\n", + "In class: derivatives Derivative on ns done! in : 0.29 s\n", + "\n", + " +++ Computing derivative on sigma8\n", + "\n", + "In class: derivatives Derivative on sigma8 done! in : 0.29 s\n", + "\n", + " +++ Computing derivative on lnbg_1\n", + "\n", + "In class: derivatives Derivative on lnbg_1 done! in : 0.06 s\n", + "\n", + " +++ Computing derivative on lnbg_2\n", + "\n", + "In class: derivatives Derivative on lnbg_2 done! in : 0.05 s\n", + "\n", + " +++ Computing derivative on lnbg_3\n", + "\n", + "In class: derivatives Derivative on lnbg_3 done! in : 0.05 s\n", + "\n", + " +++ Computing derivative on lnbg_4\n", + "\n", + "In class: derivatives Derivative on lnbg_4 done! in : 0.06 s\n", + "\n", + "In class: FisherMatrix Fisher Matrix shape: (4, 9, 9)\n", + "\n", + " Fisher matrix calculation finished in 1.74 s\n", + "\n", + " Fisher matrix exported: ./results/CosmicFish_v1.2.0_GCsp_presentation_GCsp_fishermatrix.txt\n", + "\n", + "\n", + "In class: FisherMatrix CosmicFish settings and Survey specifications exported: ./results/CosmicFish_v1.2.0_GCsp_presentation_GCsp_fishermatrix_specifications.dat\n" + ] + } + ], + "source": [ + "observables = [\"GCsp\"]\n", + "cosmoFM_g = cosmicfish.FisherMatrix(\n", + " fiducialpars=fiducial,\n", + " options=options,\n", + " observables=observables,\n", + " cosmoModel=options[\"cosmo_model\"],\n", + " surveyName=options[\"survey_name\"],\n", + ")\n", + "fish_mat_g = cosmoFM_g.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Omegam', 'Omegab', 'h', 'ns', 'sigma8', 'lnbg_1', 'lnbg_2', 'lnbg_3', 'lnbg_4', 'bI_c1', 'bI_c2']\n" + ] + } + ], + "source": [ + "params_Ig = fish_mat_Ig.get_param_names()\n", + "print(params_Ig)\n", + "params_Ig_tex = fish_mat_Ig.get_param_names_latex()\n", + "#print(params_Ig_tex)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Omegam', 'Omegab', 'h', 'ns', 'sigma8', 'bI_c1', 'bI_c2']\n", + "['Omegam', 'Omegab', 'h', 'ns', 'sigma8', 'lnbg_1', 'lnbg_2', 'lnbg_3', 'lnbg_4']\n" + ] + } + ], + "source": [ + "params_I = fish_mat_I.get_param_names()\n", + "print(params_I)\n", + "params_I_tex = fish_mat_I.get_param_names_latex()\n", + "#print(params_I_tex)\n", + "params_g = fish_mat_g.get_param_names()\n", + "print(params_g)\n", + "params_g_tex = fish_mat_g.get_param_names_latex()\n", + "#print(params_g_tex)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fish_mat_Ig shape: (11, 11)\n", + "fish_mat_I shape: (7, 7)\n", + "fish_mat_g shape: (9, 9)\n" + ] + } + ], + "source": [ + "print(f\"fish_mat_Ig shape: {fish_mat_Ig.get_fisher_matrix().shape}\")\n", + "print(f\"fish_mat_I shape: {fish_mat_I.get_fisher_matrix().shape}\")\n", + "print(f\"fish_mat_g shape: {fish_mat_g.get_fisher_matrix().shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from cosmicfishpie.analysis.fishconsumer import simple_fisher_plot\n", + "from cosmicfishpie.analysis.fishconsumer import fishtable_to_pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Omegam',\n", + " 'Omegab',\n", + " 'h',\n", + " 'ns',\n", + " 'sigma8',\n", + " 'lnbg_1',\n", + " 'lnbg_2',\n", + " 'lnbg_3',\n", + " 'lnbg_4',\n", + " 'bI_c1',\n", + " 'bI_c2']" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fish_mat_all = fish_mat_Ig + fish_mat_I + fish_mat_g\n", + "fish_mat_all.get_param_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example usage\n", + "#fisher_matrices = [fish_mat_Ig, fish_mat_I, fish_mat_g, fish_mat_all] # Your Fisher matrix objects\n", + "fisher_matrices = [fish_mat_I, fish_mat_g, fish_mat_all] # Your Fisher matrix objects\n", + "params = params_I[:5] # Parameters to plot\n", + "#labels = ['SKAO IM', 'Euclid GCsp', 'IMxGCsp','IMxGCsp+IM+GCsp'] # Optional labels\n", + "labels = ['SKAO AA4 IM', 'Euclid DR3 GCsp', 'IMxGCsp+IM+GCsp'] # Optional labels\n", + "#colors = ['orange', 'blue', 'green', 'red'] # Optional colors\n", + "colors = ['blue', 'green', 'red'] # Optional colors\n", + "\n", + "fig = simple_fisher_plot(\n", + " fisher_matrices,\n", + " params,\n", + " labels=labels,\n", + " colors=colors,\n", + " n_samples=10000,\n", + " legend=True,\n", + " #save_plot=True,\n", + " output_file='my_fisher_plot.pdf'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cosmicfishpie.analysis.fisher_plot_analysis as fpa\n", + "import cosmicfishpie.analysis.fisher_operations as fo\n", + "cfanaly = fpa.CosmicFish_FisherAnalysis(fisher_list=[fish_mat_I, fish_mat_g, fish_mat_all])\n", + "fishlist = cfanaly.get_fisher_list()\n", + "fishlist" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Omegam', 'Omegab', 'h', 'ns', 'sigma8']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "params" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "pd = fishtable_to_pandas(params, cfanaly)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----\n", + "Old Fisher Name: CosmicFish_v1.2.0_GCsp_presentation_IM_fishermatrix\n", + "New Fisher Name: SKAO AA4 IM\n", + "Parameter Omegam, fiducial: 0.320, 1-sigma error: 0.0066, percent error: 2.1%\n", + "Parameter Omegab, fiducial: 0.050, 1-sigma error: 0.0022, percent error: 4.5%\n", + "Parameter h, fiducial: 0.670, 1-sigma error: 0.0110, percent error: 1.6%\n", + "Parameter ns, fiducial: 0.960, 1-sigma error: 0.0078, percent error: 0.8%\n", + "Parameter sigma8, fiducial: 0.816, 1-sigma error: 0.0067, percent error: 0.8%\n", + "Parameter bI_c1, fiducial: 0.300, 1-sigma error: 0.0070, percent error: 2.3%\n", + "Parameter bI_c2, fiducial: 0.600, 1-sigma error: 0.0093, percent error: 1.5%\n", + "----\n", + "Old Fisher Name: CosmicFish_v1.2.0_GCsp_presentation_GCsp_fishermatrix\n", + "New Fisher Name: Euclid DR3 GCsp\n", + "Parameter Omegam, fiducial: 0.320, 1-sigma error: 0.0049, percent error: 1.5%\n", + "Parameter Omegab, fiducial: 0.050, 1-sigma error: 0.0012, percent error: 2.5%\n", + "Parameter h, fiducial: 0.670, 1-sigma error: 0.0097, percent error: 1.5%\n", + "Parameter ns, fiducial: 0.960, 1-sigma error: 0.0063, percent error: 0.7%\n", + "Parameter sigma8, fiducial: 0.816, 1-sigma error: 0.0058, percent error: 0.7%\n", + "Parameter lnbg_1, fiducial: 0.379, 1-sigma error: 0.0076, percent error: 2.0%\n", + "Parameter lnbg_2, fiducial: 0.474, 1-sigma error: 0.0076, percent error: 1.6%\n", + "Parameter lnbg_3, fiducial: 0.558, 1-sigma error: 0.0076, percent error: 1.4%\n", + "Parameter lnbg_4, fiducial: 0.641, 1-sigma error: 0.0076, percent error: 1.2%\n", + "----\n", + "Old Fisher Name: CosmicFish_v1.2.0_GCsp_presentation_GCspIM_fishermatrix_CosmicFish_v1.2.0_GCsp_presentation_IM_fishermatrix_CosmicFish_v1.2.0_GCsp_presentation_GCsp_fishermatrix\n", + "New Fisher Name: IMxGCsp+IM+GCsp\n", + "Parameter Omegam, fiducial: 0.320, 1-sigma error: 0.0035, percent error: 1.1%\n", + "Parameter Omegab, fiducial: 0.050, 1-sigma error: 0.0010, percent error: 1.9%\n", + "Parameter h, fiducial: 0.670, 1-sigma error: 0.0068, percent error: 1.0%\n", + "Parameter ns, fiducial: 0.960, 1-sigma error: 0.0044, percent error: 0.5%\n", + "Parameter sigma8, fiducial: 0.816, 1-sigma error: 0.0038, percent error: 0.5%\n", + "Parameter lnbg_1, fiducial: 0.379, 1-sigma error: 0.0050, percent error: 1.3%\n", + "Parameter lnbg_2, fiducial: 0.474, 1-sigma error: 0.0050, percent error: 1.1%\n", + "Parameter lnbg_3, fiducial: 0.558, 1-sigma error: 0.0051, percent error: 0.9%\n", + "Parameter lnbg_4, fiducial: 0.641, 1-sigma error: 0.0051, percent error: 0.8%\n", + "Parameter bI_c1, fiducial: 0.300, 1-sigma error: 0.0040, percent error: 1.3%\n", + "Parameter bI_c2, fiducial: 0.600, 1-sigma error: 0.0074, percent error: 1.2%\n" + ] + } + ], + "source": [ + "for ii, fish in enumerate(fishlist):\n", + " print(\"----\")\n", + " print(\"Old Fisher Name: \", fish.name)\n", + " fish.name = labels[ii]\n", + " print(\"New Fisher Name: \", fish.name)\n", + " sigmas = fish.get_confidence_bounds()\n", + " fidus = fish.get_param_fiducial()\n", + " parnames = fish.get_param_names()\n", + " #fiww = fo.marginalise(fish, parstomarg)\n", + " #deFoM = np.sqrt(fiww.determinant())\n", + " #print(\"Fisher FoM: \", deFoM)\n", + " for ii, par in enumerate(parnames):\n", + " print(\"Parameter {:s}, fiducial: {:.3f}, 1-sigma error: {:.4f}, percent error: {:.1f}%\".format(\n", + " par, fidus[ii], abs(sigmas[ii]), abs(100*sigmas[ii]/fidus[ii])))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fishlist" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "import cosmicfishpie.analysis.fisher_plotting as fplt" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./plots exists already\n", + "Fisher matrix loaded, label name: IMxGCsp+IM+GCsp\n", + "Fisher matrix loaded, label name: SKAO IM\n", + "Fisher matrix loaded, label name: Euclid DR3 GCsp\n" + ] + } + ], + "source": [ + "plot_options = {'fishers_list': [fish_mat_all, fish_mat_I, fish_mat_g], \n", + " 'colors': snscolors,\n", + " 'fish_labels': ['IMxGCsp+IM+GCsp', 'SKAO IM', 'Euclid DR3 GCsp'],\n", + " 'filled': False,\n", + " 'plot_pars': parnames[:5],\n", + " 'axis_custom_factors': {'all':3}, ## Axis limits cover 3-sigma bounds of first Fisher matrix\n", + " 'plot_method': 'Gaussian',\n", + " 'file_format': '.pdf', ##file format for all the plots\n", + " 'outpath' : './plots/', ## directory where to store the files, if non-existent, it will be created\n", + " 'outroot':'SKAO-Euclid_IM-GCsp' ## file name root for all the plots, extra names can be added individually\n", + " } \n", + "\n", + "\n", + "fish_plotter = fplt.fisher_plotting(**plot_options)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Fishers names: ', ['IMxGCsp+IM+GCsp', 'SKAO IM', 'Euclid DR3 GCsp'])\n", + "('parameters to plot: ', ['Omegam', 'Omegab', 'h', 'ns', 'sigma8'])\n", + "X tick labels ---> : ['\\\\Omega_{{\\\\rm m}, 0}', '\\\\Omega_{{\\\\rm b}, 0}', 'h', 'n_{\\\\rm s}', '\\\\sigma_8']\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fish_plotter.compare_errors({'yrang':[-100, 100], 'compare_to_index': 2})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cosmicfish", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 4b73bd257c2c115d9cbb1aae5de84e2ff8fd4972 Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Sun, 1 Dec 2024 13:47:16 +0100 Subject: [PATCH 5/8] fix style --- cosmicfishpie/CMBsurvey/CMB_cov.py | 2 +- cosmicfishpie/LSSsurvey/photo_cov.py | 2 +- cosmicfishpie/LSSsurvey/photo_window.py | 2 +- cosmicfishpie/LSSsurvey/spectro_cov.py | 60 +++++++------- cosmicfishpie/LSSsurvey/spectro_obs.py | 32 ++++---- cosmicfishpie/analysis/fishconsumer.py | 43 +++++----- .../analysis/fisher_plot_analysis.py | 8 +- cosmicfishpie/analysis/utilities.py | 2 +- cosmicfishpie/configs/config.py | 81 ++++++++++--------- cosmicfishpie/cosmology/cosmology.py | 24 +++--- cosmicfishpie/cosmology/nuisance.py | 21 +++-- cosmicfishpie/fishermatrix/cosmicfish.py | 4 +- cosmicfishpie/fishermatrix/derivatives.py | 4 +- cosmicfishpie/utilities/utils.py | 6 +- 14 files changed, 153 insertions(+), 138 deletions(-) diff --git a/cosmicfishpie/CMBsurvey/CMB_cov.py b/cosmicfishpie/CMBsurvey/CMB_cov.py index 08d11d6..cd25bc5 100644 --- a/cosmicfishpie/CMBsurvey/CMB_cov.py +++ b/cosmicfishpie/CMBsurvey/CMB_cov.py @@ -97,7 +97,7 @@ def getclsnoise(self, cls): ] Bell = [ - np.exp(ang**2.0 * noisy_cls["ells"] * (noisy_cls["ells"] + 1) / 2.0) for ang in thetab + np.exp(ang ** 2.0 * noisy_cls["ells"] * (noisy_cls["ells"] + 1) / 2.0) for ang in thetab ] wtemp = [ diff --git a/cosmicfishpie/LSSsurvey/photo_cov.py b/cosmicfishpie/LSSsurvey/photo_cov.py index 3a54843..c9d57f0 100644 --- a/cosmicfishpie/LSSsurvey/photo_cov.py +++ b/cosmicfishpie/LSSsurvey/photo_cov.py @@ -142,7 +142,7 @@ def getclsnoise(self, cls): ) elif obs == "WL": noisy_cls[obs + " " + str(ind) + "x" + obs + " " + str(ind)] += ( - self.ellipt_error**2.0 + self.ellipt_error ** 2.0 ) / self.ngalbin[ind - 1] return noisy_cls diff --git a/cosmicfishpie/LSSsurvey/photo_window.py b/cosmicfishpie/LSSsurvey/photo_window.py index 1672f75..98f62a7 100644 --- a/cosmicfishpie/LSSsurvey/photo_window.py +++ b/cosmicfishpie/LSSsurvey/photo_window.py @@ -78,7 +78,7 @@ def dNdz(self, z): pref = z / self.z0_p expo = z / self.z0 - return pref**2 * np.exp(-(expo**self.ngamma)) + return pref ** 2 * np.exp(-(expo ** self.ngamma)) def n_i(self, z, i): """Function to compute the unnormalized dN/dz(z) with a window picking function applied to it diff --git a/cosmicfishpie/LSSsurvey/spectro_cov.py b/cosmicfishpie/LSSsurvey/spectro_cov.py index 8017c46..95cbcb5 100644 --- a/cosmicfishpie/LSSsurvey/spectro_cov.py +++ b/cosmicfishpie/LSSsurvey/spectro_cov.py @@ -18,8 +18,7 @@ class SpectroCov: def __init__( - self, fiducialpars, fiducial_specobs=None, bias_samples=["g", "g"], - configuration=None + self, fiducialpars, fiducial_specobs=None, bias_samples=["g", "g"], configuration=None ): """ Initializes an object with specified fiducial parameters and computes @@ -75,11 +74,11 @@ def __init__( self.fsky_spectro = self.area_survey / upm.areasky() if fiducial_specobs is None: self.pk_obs = spec_obs.ComputeGalSpectro( - fiducialpars, - fiducial_cosmopars=fiducialpars, - bias_samples=bias_samples, - configuration=self.config - ) + fiducialpars, + fiducial_cosmopars=fiducialpars, + bias_samples=bias_samples, + configuration=self.config, + ) # if no other parameters are provided, the method will use the fiducials from config else: self.pk_obs = fiducial_specobs @@ -216,7 +215,7 @@ def veff(self, zi, k, mu): The effective volume for a given wavenumber, angle and redshift """ npobs = self.n_density(zi) * self.pk_obs.observed_Pgg(zi, k, mu) - prefactor = 1 / (8 * (np.pi**2)) + prefactor = 1 / (8 * (np.pi ** 2)) covterm = prefactor * (npobs / (1 + npobs)) ** 2 if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: covterm = np.zeros_like(covterm) @@ -272,14 +271,16 @@ def P_noise_21(self, z, k, mu, temp_dim=True, beam_term=False): elif temp_dim: temp = 1 pref = (2 * np.pi * self.pk_obs.fsky_IM) / (self.pk_obs.f_21 * self.pk_obs.t_tot) - cosmo = ((1 + z) ** 2 * self.pk_obs.fiducialcosmo.comoving(z) ** 2) / self.pk_obs.fiducialcosmo.Hubble(z) + cosmo = ( + (1 + z) ** 2 * self.pk_obs.fiducialcosmo.comoving(z) ** 2 + ) / self.pk_obs.fiducialcosmo.Hubble(z) T_term = (self.Tsys_func(z) / temp) ** 2 # in K alpha = self.pk_obs.alpha_SD() if beam_term: beta = self.pk_obs.beta_SD(z, k, mu) else: beta = np.ones_like(k) - noise = pref * cosmo * T_term * (alpha / beta**2) + noise = pref * cosmo * T_term * (alpha / beta ** 2) return noise def veff_II(self, zi, k, mu): @@ -300,7 +301,7 @@ def veff_II(self, zi, k, mu): """ pobs = self.pk_obs.observed_P_ij(zi, k, mu, si="I", sj="I") pnoisy = self.noisy_P_ij(zi, k, mu, si="I", sj="I") - prefactor = 1 / (8 * (np.pi**2)) + prefactor = 1 / (8 * (np.pi ** 2)) covterm = prefactor * (pobs / pnoisy) ** 2 if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: covterm = np.zeros_like(covterm) @@ -329,24 +330,25 @@ def veff_Ig(self, zi, k, mu): pnoisy_Ig = self.noisy_P_ij(zi, k, mu, si="I", sj="g") pnoisy_II = self.noisy_P_ij(zi, k, mu, si="I", sj="I") pnoisy_gg = self.noisy_P_ij(zi, k, mu, si="g", sj="g") - covterm = pobs_Ig**2 / (pnoisy_gg * pnoisy_II + pnoisy_Ig * pnoisy_Ig) - prefactor = 1 / (4 * (np.pi**2)) + covterm = pobs_Ig ** 2 / (pnoisy_gg * pnoisy_II + pnoisy_Ig * pnoisy_Ig) + prefactor = 1 / (4 * (np.pi ** 2)) covterm = prefactor * covterm if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: covterm = np.zeros_like(covterm) return covterm - + def noisy_P_ij(self, z, k, mu, si="I", sj="g"): if si == "I" and sj == "I": noiseterm = self.P_noise_21(z, k, mu, temp_dim=True) elif si == "g" and sj == "g": - noiseterm = 1/self.n_density(z) + noiseterm = 1 / self.n_density(z) else: noiseterm = 0 pobs_ij = self.pk_obs.observed_P_ij(z, k, mu, si=si, sj=sj) pnoisy_ij = pobs_ij + noiseterm return pnoisy_ij + class SpectroDerivs: def __init__( self, @@ -440,16 +442,16 @@ def initialize_obs(self, allpars): else: IMbiaspars = None self.pobs = spec_obs.ComputeGalSpectro( - cosmopars=cosmopars, - fiducial_cosmopars=self.fiducial_cosmopars, - spectrobiaspars=spectrobiaspars, - spectrononlinearpars=spectrononlinearpars, - PShotpars=PShotpars, - IMbiaspars=IMbiaspars, - fiducial_cosmo=self.fiducial_cosmo, - bias_samples=self.bias_samples, - configuration=self.config, - ) + cosmopars=cosmopars, + fiducial_cosmopars=self.fiducial_cosmopars, + spectrobiaspars=spectrobiaspars, + spectrononlinearpars=spectrononlinearpars, + PShotpars=PShotpars, + IMbiaspars=IMbiaspars, + fiducial_cosmo=self.fiducial_cosmo, + bias_samples=self.bias_samples, + configuration=self.config, + ) strdic = str(sorted(cosmopars.items())) hh = hash(strdic) self.cosmology_variations_dict[hh] = self.pobs.cosmo @@ -473,11 +475,15 @@ def get_obs(self, allpars): result_array["z_bins"] = self.z_array for ii, zzi in enumerate(self.z_array): if self.bias_samples == ["I", "I"]: - result_array[ii] = self.pobs.lnpobs_ij(zzi, self.pk_kmesh, self.pk_mumesh, si="I", sj="I") + result_array[ii] = self.pobs.lnpobs_ij( + zzi, self.pk_kmesh, self.pk_mumesh, si="I", sj="I" + ) elif self.bias_samples == ["g", "g"]: result_array[ii] = self.pobs.lnpobs_gg(zzi, self.pk_kmesh, self.pk_mumesh) elif self.bias_samples == ["I", "g"] or self.bias_samples == ["g", "I"]: - result_array[ii] = self.pobs.lnpobs_ij(zzi, self.pk_kmesh, self.pk_mumesh, si="I", sj="g") + result_array[ii] = self.pobs.lnpobs_ij( + zzi, self.pk_kmesh, self.pk_mumesh, si="I", sj="g" + ) return result_array def exact_derivs(self, par): diff --git a/cosmicfishpie/LSSsurvey/spectro_obs.py b/cosmicfishpie/LSSsurvey/spectro_obs.py index f4f6dc3..c77f8bb 100644 --- a/cosmicfishpie/LSSsurvey/spectro_obs.py +++ b/cosmicfishpie/LSSsurvey/spectro_obs.py @@ -166,7 +166,7 @@ def __init__( self.IMbiaspars = self.fiducial_IMbiaspars else: self.IMbiaspars = IMbiaspars - + self.nuisance = nuisance.Nuisance( configuration=self.config, spectrobiasparams=self.spectrobiaspars, @@ -211,7 +211,7 @@ def __init__( if "IM" in self.observables: self.set_IM_specs() self.set_IM_bias_specs() - + tend = time() upt.time_print( feedback_level=self.feed_lvl, @@ -376,7 +376,7 @@ def kper(self, z, k, mu): Observed perpendicular projection of wavevector onto the line of sight with AP-effect corrected for """ - k_per = k * np.sqrt(1 - mu**2) * (1 / self.qperpendicular(z)) + k_per = k * np.sqrt(1 - mu ** 2) * (1 / self.qperpendicular(z)) return k_per def k_units_change(self, k): @@ -471,7 +471,7 @@ def spec_err_z(self, z, k, mu): elif self.dz_type == "z-dependent": spec_dz_err = self.dz_err * (1 + z) err = spec_dz_err * (1 / self.cosmo.Hubble(z)) * self.kpar(z, k, mu) - return np.exp(-(1 / 2) * err**2) + return np.exp(-(1 / 2) * err ** 2) def BAO_term(self, z): """Calculates the BAO term. This is the rescaling of the Fourier volume by the AP-effect @@ -538,7 +538,7 @@ def bterm_fid(self, z, k=None, bias_sample="g"): raise ValueError( f"Bias sample {bias_sample} not found. " f"Please use {self.IM_bias_sample} bias sample." - ) + ) bterm_z = self.nuisance.IM_bias_at_z(z) bterm_k = self.nuisance.gcsp_bias_kscale(k) bterm_zk = bterm_z * bterm_k @@ -593,9 +593,9 @@ def kaiserTerm(self, z, k, mu, b_i=None, just_rsd=False, bias_sample="g"): fterm = self.cosmo.f_growthrate(z, k, tracer=self.tracer) if not just_rsd: - kaiser = bterm + fterm * mu**2 + kaiser = bterm + fterm * mu ** 2 elif just_rsd: - kaiser = 1 + (fterm / bterm) * mu**2 + kaiser = 1 + (fterm / bterm) * mu ** 2 return kaiser @@ -679,7 +679,7 @@ def sigmavNL(self, zz, mu): f0 = self.P_ThetaTheta_Moments(zz, 0) f1 = self.P_ThetaTheta_Moments(zz, 1) f2 = self.P_ThetaTheta_Moments(zz, 2) - sv = np.sqrt(f0 + 2 * mu**2 * f1 + mu**2 * f2) + sv = np.sqrt(f0 + 2 * mu ** 2 * f1 + mu ** 2 * f2) if self.vary_sigmav: sv *= self.nuisance.vectorized_gcsp_rescale_sigmapv_at_z(zz, sigma_key="sigmav") return sv @@ -714,7 +714,7 @@ def f_mom(k): pp = cosmoF.matpow(zz, self.k_grid).flatten() integrand = pp * ff Int = integrate.trapezoid(integrand, x=self.k_grid) - ptt = (1 / (6 * np.pi**2)) * Int + ptt = (1 / (6 * np.pi ** 2)) * Int return ptt def normalized_pdd(self, z, k): @@ -800,8 +800,8 @@ def dewiggled_pdd(self, z, k, mu): self.p_dd = self.normalized_pdd(z, k) self.p_dd_NW = self.normalized_pnw(z, k) - self.p_dd_DW = self.p_dd * np.exp(-gmudamping * k**2) + self.p_dd_NW * ( - 1 - np.exp(-gmudamping * k**2) + self.p_dd_DW = self.p_dd * np.exp(-gmudamping * k ** 2) + self.p_dd_NW * ( + 1 - np.exp(-gmudamping * k ** 2) ) return self.p_dd_DW @@ -860,7 +860,7 @@ def observed_Pgg(self, z, k, mu, b_i=None): lorentzFoG = self.FingersOfGod(z, k, mu, mode="Lorentz") p_dd_DW = self.dewiggled_pdd(z, k, mu) - pgg_obs = baoterm * (kaiser**2) * p_dd_DW * lorentzFoG * (error_z**2) + extra_shotnoise + pgg_obs = baoterm * (kaiser ** 2) * p_dd_DW * lorentzFoG * (error_z ** 2) + extra_shotnoise tend = time() upt.time_print( @@ -940,7 +940,7 @@ def beta_SD(self, z, k, mu): tol = 1.0e-12 k = np.atleast_1d(k) mu = np.atleast_1d(mu) - expo = k**2 * (1 - mu**2) * self.fiducialcosmo.comoving(z) ** 2 * self.theta_b(z) ** 2 + expo = k ** 2 * (1 - mu ** 2) * self.fiducialcosmo.comoving(z) ** 2 * self.theta_b(z) ** 2 bet = np.exp(-expo / (16.0 * np.log(2.0))) bet[np.abs(bet) < tol] = tol return bet @@ -949,9 +949,11 @@ def observed_P_ij(self, z, k, mu, bsi_z=None, bsj_z=None, si="I", sj="g"): error_z = self.spec_err_z(z, k, mu) beam_damping_term_si = self.beta_SD(z, k, mu) if si == "I" else 1 beam_damping_term_sj = self.beta_SD(z, k, mu) if sj == "I" else 1 - k = self.k_units_change(k) # h-bug set to False by default, leaving here for cross-check of old cases + k = self.k_units_change( + k + ) # h-bug set to False by default, leaving here for cross-check of old cases k, mu = self.kmu_alc_pac(z, k, mu) - #if self.bias_samples is not None: + # if self.bias_samples is not None: # si = self.bias_samples[0] # sj = self.bias_samples[1] baoterm = self.BAO_term(z) diff --git a/cosmicfishpie/analysis/fishconsumer.py b/cosmicfishpie/analysis/fishconsumer.py index 17bac5a..85b436f 100644 --- a/cosmicfishpie/analysis/fishconsumer.py +++ b/cosmicfishpie/analysis/fishconsumer.py @@ -101,7 +101,7 @@ def display_colors(colors, figsize=(6, 6)): # rotation_angle = angle x1, y1 = wedges[i].center x2, y2 = np.cos(np.deg2rad(angle)), np.sin(np.deg2rad(angle)) - dx, dy = 1.2 * np.array([x2, y2]) / np.sqrt(x2**2 + y2**2) + dx, dy = 1.2 * np.array([x2, y2]) / np.sqrt(x2 ** 2 + y2 ** 2) ax.annotate( color, xy=(x1, y1), @@ -627,8 +627,7 @@ def prepare_settings_plot( def chainfishplot( - return_dictionary, - **cckwargs, + return_dictionary, **cckwargs, ): """ Chain fish plot function @@ -776,6 +775,7 @@ def chainfishplot( print("Plot saved to: ", plotfilename) return fig + def simple_fisher_plot( fisher_list, params_to_plot, @@ -784,7 +784,7 @@ def simple_fisher_plot( save_plot=False, legend=True, n_samples=10000, - output_file="fisher_plot.pdf" + output_file="fisher_plot.pdf", ): """Create a triangle plot from Fisher matrices using ChainConsumer. @@ -810,34 +810,27 @@ def simple_fisher_plot( """ # Initialize ChainConsumer c = ChainConsumer() - + # Default colors if none provided if colors is None: - colors = ['#3a86ff', '#fb5607', '#8338ec', '#ffbe0b', '#d11149'] - colors = colors[:len(fisher_list)] # Truncate to needed length - + colors = ["#3a86ff", "#fb5607", "#8338ec", "#ffbe0b", "#d11149"] + colors = colors[: len(fisher_list)] # Truncate to needed length + # Default labels if none provided if labels is None: labels = [f"Fisher {i+1}" for i in range(len(fisher_list))] - + # Generate samples for each Fisher matrix n_samples = 100000 for i, fisher in enumerate(fisher_list): # Get samples from multivariate normal using Fisher matrix samples = multivariate_normal( - fisher.param_fiducial, - fisher.inverse_fisher_matrix(), - size=n_samples + fisher.param_fiducial, fisher.inverse_fisher_matrix(), size=n_samples ) - + # Add chain to plot - c.add_chain( - samples, - parameters=fisher.get_param_names(), - name=labels[i], - color=colors[i] - ) - + c.add_chain(samples, parameters=fisher.get_param_names(), name=labels[i], color=colors[i]) + # Configure plot settings c.configure( plot_hists=True, @@ -848,15 +841,15 @@ def simple_fisher_plot( shade_alpha=0.3, bar_shade=True, linewidths=2, - legend_kwargs={"fontsize": 12} + legend_kwargs={"fontsize": 12}, ) - + # Create the plot fig = c.plotter.plot(parameters=params_to_plot, legend=legend) - + # Save if requested if save_plot: - fig.savefig(output_file, bbox_inches='tight', dpi=200) + fig.savefig(output_file, bbox_inches="tight", dpi=200) print(f"Plot saved to: {output_file}") - + return fig diff --git a/cosmicfishpie/analysis/fisher_plot_analysis.py b/cosmicfishpie/analysis/fisher_plot_analysis.py index 17c98f4..7486886 100644 --- a/cosmicfishpie/analysis/fisher_plot_analysis.py +++ b/cosmicfishpie/analysis/fisher_plot_analysis.py @@ -663,14 +663,14 @@ def compute_gaussian( if normalized: y_points = np.array( [ - np.exp(-((x - fiducial) ** 2) / (2.0 * sigma**2)) + np.exp(-((x - fiducial) ** 2) / (2.0 * sigma ** 2)) / (sigma * np.sqrt(2.0 * math.pi)) for x in x_points ] ) else: y_points = np.array( - [np.exp(-((x - fiducial) ** 2) / (2.0 * sigma**2)) for x in x_points] + [np.exp(-((x - fiducial) ** 2) / (2.0 * sigma ** 2)) for x in x_points] ) dict_names[name] = [x_points, y_points, [fiducial, sigma]] @@ -749,11 +749,11 @@ def compute_ellipse( # compute the ellipse coefficients: coeff_a = confidence_coefficient * math.sqrt( (sigma_x + sigma_y) / 2.0 - + math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy**2) + + math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy ** 2) ) coeff_b = confidence_coefficient * math.sqrt( (sigma_x + sigma_y) / 2.0 - - math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy**2) + - math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy ** 2) ) theta_0 = math.atan2((2.0 * sigma_xy), (sigma_x - sigma_y)) / 2.0 # generate the ellipses diff --git a/cosmicfishpie/analysis/utilities.py b/cosmicfishpie/analysis/utilities.py index dafae55..93d8f5f 100644 --- a/cosmicfishpie/analysis/utilities.py +++ b/cosmicfishpie/analysis/utilities.py @@ -43,7 +43,7 @@ def num_to_mant_exp(num): exponent = math.floor(math.log10(abs(num))) except ValueError: # Case of log10(0) return (0, 0) # Convention: 0 = 0*10^0 - mantissa = num / 10**exponent + mantissa = num / 10 ** exponent return (mantissa, int(exponent)) diff --git a/cosmicfishpie/configs/config.py b/cosmicfishpie/configs/config.py index 2d54b11..df40a60 100644 --- a/cosmicfishpie/configs/config.py +++ b/cosmicfishpie/configs/config.py @@ -14,6 +14,7 @@ from cosmicfishpie.utilities.utils import physmath as upm from cosmicfishpie.utilities.utils import printing as upt + def init( options=dict(), specifications=dict(), @@ -207,18 +208,15 @@ def init( # Set defaults if not contained previously in options settings.setdefault( "specs_dir_default", - os.path.join( - os.path.dirname(os.path.realpath(__file__)), - "default_survey_specifications", - ), + os.path.join(os.path.dirname(os.path.realpath(__file__)), "default_survey_specifications",), ) settings.setdefault("specs_dir", settings["specs_dir_default"]) settings.setdefault("survey_name", surveyName) settings.setdefault("survey_specs", "ISTF-Optimistic") settings.setdefault("survey_name_photo", "Euclid-Photometric-ISTF-Pessimistic") settings.setdefault("survey_name_spectro", "Euclid-Spectroscopic-ISTF-Pessimistic") - #settings.setdefault("survey_name_radio_photo", "SKA1-Photometric-Redbook-Optimistic") - #settings.setdefault("survey_name_radio_spectro", "SKA1-Spectroscopic-Redbook-Optimistic") + # settings.setdefault("survey_name_radio_photo", "SKA1-Photometric-Redbook-Optimistic") + # settings.setdefault("survey_name_radio_spectro", "SKA1-Spectroscopic-Redbook-Optimistic") settings.setdefault("survey_name_radio_IM", "SKA1-IM-Redbook-Optimistic") settings.setdefault("fail_on_specs_not_found", False) settings.setdefault("derivatives", "3PT") @@ -368,9 +366,12 @@ def ngal_per_bin(ngal_sqarmin, zbins): def create_ph_dict(foldername, filename): photo_dict = dict() - if filename==False: - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> No photo survey passed, returning empty dict") + if filename == False: + upt.time_print( + feedback_level=feed_lvl, + min_level=1, + text=f"-> No photo survey passed, returning empty dict", + ) return photo_dict try: ph_file_path = os.path.join(foldername, filename + ".yaml") @@ -404,9 +405,12 @@ def create_ph_dict(foldername, filename): def create_sp_dict(foldername, filename, type="spectro"): spec_dict = dict() - if filename==False: - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> No {type} survey passed, returning empty dict") + if filename == False: + upt.time_print( + feedback_level=feed_lvl, + min_level=1, + text=f"-> No {type} survey passed, returning empty dict", + ) return spec_dict try: sp_file_path = os.path.join(foldername, filename + ".yaml") @@ -449,57 +453,64 @@ def create_sp_dict(foldername, filename, type="spectro"): specificationsf1 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) specificationsf.update(specificationsf1) spectroTaken = True - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> Survey loaded: {surveyNameSpectro}") + upt.time_print( + feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNameSpectro}" + ) surveyNamePhoto = settings.get("survey_name_photo") if surveyNamePhoto: specificationsf2 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) specificationsf.update(specificationsf2) photoTaken = True - upt.time_print(feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNamePhoto}") + upt.time_print( + feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNamePhoto}" + ) if "SKA" in surveyName: surveyNameRadioIM = settings.get("survey_name_radio_IM") specificationsf3 = create_sp_dict(settings["specs_dir"], surveyNameRadioIM, type="IM") specificationsf.update(specificationsf3) - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> Survey loaded: {surveyNameRadioIM}") - if spectroTaken==False: + upt.time_print( + feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNameRadioIM}" + ) + if spectroTaken == False: surveyNameSpectro = settings.get("survey_name_spectro") specificationsf4 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) specificationsf.update(specificationsf4) spectroTaken = True - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> Survey loaded: {surveyNameSpectro}") - if photoTaken==False: + upt.time_print( + feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNameSpectro}" + ) + if photoTaken == False: surveyNamePhoto = settings.get("survey_name_photo") specificationsf5 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) specificationsf.update(specificationsf5) photoTaken = True - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> Survey loaded: {surveyNamePhoto}") + upt.time_print( + feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNamePhoto}" + ) if "Rubin" in surveyName: - if photoTaken==False: + if photoTaken == False: surveyNamePhoto = settings.get("survey_name_photo") specificationsf6 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) specificationsf.update(specificationsf6) photoTaken = True - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> Survey loaded: {surveyNamePhoto}") + upt.time_print( + feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNamePhoto}" + ) if "DESI" in surveyName: - if spectroTaken==False: + if spectroTaken == False: surveyNameSpectro = settings.get("survey_name_spectro") specificationsf7 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) specificationsf.update(specificationsf7) spectroTaken = True - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> Survey loaded: {surveyNameSpectro}") + upt.time_print( + feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNameSpectro}" + ) if "Planck" in surveyName: yaml_file = open(os.path.join(settings["specs_dir"], "Planck.yaml")) parsed_yaml_file = yaml.load(yaml_file, Loader=yaml.FullLoader) specificationsfPlanck = parsed_yaml_file["specifications"] specificationsf.update(specificationsfPlanck) - upt.time_print(feedback_level=feed_lvl, min_level=1, - text=f"-> Survey loaded: Planck") + upt.time_print(feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: Planck") if surveyName not in available_survey_names: print("Survey name passed: ", surveyName) print( @@ -524,7 +535,7 @@ def create_sp_dict(foldername, filename, type="spectro"): specs["fsky_IM"] = specificationsf.get( "fsky_IM", upm.sqdegtofsky(specificationsf.get("area_survey_IM", 0.0)) ) - #ums.deepupdate(specs, specifications) # deep update keys if passed by users + # ums.deepupdate(specs, specifications) # deep update keys if passed by users specs.update(specifications) specs["survey_name"] = surveyName specs["specs_dir"] = settings["specs_dir"] # Path for additional files like luminosity @@ -587,9 +598,7 @@ def create_sp_dict(foldername, filename, type="spectro"): global fiducialcosmo upt.time_print( - feedback_level=feed_lvl, - min_level=1, - text="-> Computing cosmology at the fiducial point", + feedback_level=feed_lvl, min_level=1, text="-> Computing cosmology at the fiducial point", ) tcosmo1 = time() fiducialcosmo = cosmology.cosmo_functions(fiducialparams, input_type) @@ -719,7 +728,7 @@ def create_sp_dict(foldername, filename, type="spectro"): for key in PShotparams: freeparams.setdefault(key, default_eps_gc_nuis) for key in Spectrononlinearparams: - freeparams.setdefault(key, default_eps_gc_nonlin) + freeparams.setdefault(key, default_eps_gc_nonlin) # Only add the free parameters that are not already in the dictionary if "GCsp" in obs: for key in Spectrobiasparams: diff --git a/cosmicfishpie/cosmology/cosmology.py b/cosmicfishpie/cosmology/cosmology.py index e82d49e..f10fcd2 100644 --- a/cosmicfishpie/cosmology/cosmology.py +++ b/cosmicfishpie/cosmology/cosmology.py @@ -301,10 +301,10 @@ def compute_sigma8(z_range, pk_interpolator, h_value, k_range): 9 * (k * R * np.cos(k * R) - np.sin(k * R)) ** 2 * pk_z[i] - / k**4 - / R**6 + / k ** 4 + / R ** 6 / 2 - / np.pi**2 + / np.pi ** 2 ) sigma_z[i] = np.sqrt(integrate.trapezoid(integrand, k)) @@ -426,7 +426,7 @@ def changebasis_camb(self, cosmopars, camb): g_factor = fidNeff / 3 neutrino_mass_fac = boltzmann_code.hardcoded_neutrino_mass_fac - h2 = self.h_now**2 + h2 = self.h_now ** 2 if "mnu" in cambpars: Onu = cambpars["mnu"] / neutrino_mass_fac * (g_factor) ** 0.75 / h2 @@ -611,11 +611,11 @@ def camb_results(self, camb): ) Pk_cb_nl = ( 1 - / f_cb**2 + / f_cb ** 2 * ( Pk_nl.P(self.results.zgrid, self.results.kgrid) - 2 * Pk_cross_l.P(self.results.zgrid, self.results.kgrid) * f_cb * f_nu - - Pk_nunu_l.P(self.results.zgrid, self.results.kgrid) * f_nu**2 + - Pk_nunu_l.P(self.results.zgrid, self.results.kgrid) * f_nu ** 2 ) ) self.results.Pk_cb_nl = RectBivariateSpline( @@ -756,7 +756,7 @@ def changebasis_class(self, cosmopars): if "mnu" in classpars: classpars["T_ncdm"] = (4.0 / 11.0) ** (1.0 / 3.0) * g_factor ** (1.0 / 4.0) classpars["Omega_ncdm"] = ( - classpars["mnu"] * g_factor ** (0.75) / neutrino_mass_fac / h**2 + classpars["mnu"] * g_factor ** (0.75) / neutrino_mass_fac / h ** 2 ) classpars.pop("mnu") # classpars['m_ncdm'] = classpars.pop('mnu') @@ -844,13 +844,13 @@ def class_results(self, Class): pm = classres.get_primordial() pk_prim = ( UnivariateSpline(pm["k [1/Mpc]"], pm["P_scalar(k)"])(k) - * (2.0 * np.pi**2) + * (2.0 * np.pi ** 2) / np.power(k, 3) ) pk_cnu = T_nu * T_cb * pk_prim[:, None] pk_nunu = T_nu * T_nu * pk_prim[:, None] - Pk_cb_nl = 1.0 / f_cb**2 * (Pk_nl - 2 * pk_cnu * f_nu * f_cb - pk_nunu * f_nu * f_nu) + Pk_cb_nl = 1.0 / f_cb ** 2 * (Pk_nl - 2 * pk_cnu * f_nu * f_cb - pk_nunu * f_nu * f_nu) self.results.Pk_cb_nl = RectBivariateSpline( z[::-1], k, (np.flip(Pk_cb_nl, axis=1)).transpose() @@ -952,9 +952,9 @@ def changebasis_symb(self, cosmopars): # ["sigma8", "As", "logAs", "10^9As", "ln_A_s_1e10"] try: if "As" in symbpars: - symbpars["10^9As"] = 10**9 * symbpars.pop("As") + symbpars["10^9As"] = 10 ** 9 * symbpars.pop("As") if "logAs" in symbpars: - symbpars["10^9As"] = 10**9 * (np.exp(symbpars.pop("logAs")) * 1.0e-10) + symbpars["10^9As"] = 10 ** 9 * (np.exp(symbpars.pop("logAs")) * 1.0e-10) try: As_value = symbpars.get("10^9As") upr.debug_print("DEBUG: symbpars['10^9As'] = ", As_value) @@ -1096,7 +1096,7 @@ def symbolic_results(self): extrapolate=self.extrapolate, ) # symbfit plin_emulated returns P_l(k,z=0) in 1/Mpc^3, requests kgrid in h/Mpc - Pk_at_z = (D_kz**2) * self.results.Pk_l_0 + Pk_at_z = (D_kz ** 2) * self.results.Pk_l_0 self.results.Pk_l = RectBivariateSpline( self.zgrid, self.kgrid_1Mpc, Pk_at_z ) # P_l(k,z) in 1/Mpc^3 diff --git a/cosmicfishpie/cosmology/nuisance.py b/cosmicfishpie/cosmology/nuisance.py index 5cafbe9..232f7e5 100644 --- a/cosmicfishpie/cosmology/nuisance.py +++ b/cosmicfishpie/cosmology/nuisance.py @@ -33,8 +33,13 @@ class Nuisance: - def __init__(self, configuration=None, spectrobiasparams=None, - spectrononlinearpars=None, IMbiasparams=None): + def __init__( + self, + configuration=None, + spectrobiasparams=None, + spectrononlinearpars=None, + IMbiasparams=None, + ): if configuration is None: self.config = cfg else: @@ -69,7 +74,7 @@ def __init__(self, configuration=None, spectrobiasparams=None, else: self.IMbiasparams = IMbiasparams if self.IM_bias_model == "fitting": - self.IM_bias_at_z = self.IM_bias_fitting + self.IM_bias_at_z = self.IM_bias_fitting else: print("Not implemented bias model for IM") raise ValueError(f"IM bias model {self.IM_bias_model} not implemented") @@ -291,7 +296,7 @@ def gcsp_bias_kscale(self, k, z=None): default_A1 = 0.0 default_A2 = 0.0 try: - bterm_k = (1 + k**2 * self.Spectrobiasparams.get("A2", default_A2)) / ( + bterm_k = (1 + k ** 2 * self.Spectrobiasparams.get("A2", default_A2)) / ( 1 + k * self.Spectrobiasparams.get("A1", default_A1) ) except KeyError as ke: @@ -358,11 +363,11 @@ def IM_zbins_func(self): zbins = np.unique(np.concatenate(zbins)) self.IM_zbins_inds = zbin_inds return zbins - + def IM_zbins_mids_func(self): IM_zbins_mids = unu.moving_average(self.IM_zbins) return IM_zbins_mids - + def IM_bias_fitting(self, z): """ IM 21cm HI bias function from http://arxiv.org/abs/2006.05996 @@ -394,7 +399,7 @@ def IM_THI_noise(self): System noise temperature values corresponding to the redshift points """ THI_sys = self.specs["THI_sys_noise"] - z_vals_THI = THI_sys['z_vals_THI'] - THI_vals = THI_sys['THI_sys_noise'] + z_vals_THI = THI_sys["z_vals_THI"] + THI_vals = THI_sys["THI_sys_noise"] Tsys_interp = UnivariateSpline(z_vals_THI, THI_vals) return Tsys_interp diff --git a/cosmicfishpie/fishermatrix/cosmicfish.py b/cosmicfishpie/fishermatrix/cosmicfish.py index 6054124..9fb41bc 100644 --- a/cosmicfishpie/fishermatrix/cosmicfish.py +++ b/cosmicfishpie/fishermatrix/cosmicfish.py @@ -271,7 +271,7 @@ def compute(self, max_z_bins=None): spectrononlinearpars=self.Spectrononlinpars, IMbiaspars=self.IMbiaspars, PShotpars=self.PShotpars, - # bias_samples=self.obs_spectrum, + # bias_samples=self.obs_spectrum, configuration=self, ) self.pk_cov = spectro_cov.SpectroCov( @@ -535,7 +535,7 @@ def fish_integrand(self, zi, k, mu, pi, pj): # zmid = self.pk_cov.global_z_bin_mids[zi] dPdpi = self.derivs_dict[pi][zi] dPdpj = self.derivs_dict[pj][zi] - intg = k**2 * pref * volsurv * self.veff_arr[zi] * dPdpi * dPdpj + intg = k ** 2 * pref * volsurv * self.veff_arr[zi] * dPdpi * dPdpj return intg def photo_LSS_fishermatrix(self, noisy_cls=None, covmat=None, derivs=None, lss_obj=None): diff --git a/cosmicfishpie/fishermatrix/derivatives.py b/cosmicfishpie/fishermatrix/derivatives.py index 9a014c9..1bacd11 100644 --- a/cosmicfishpie/fishermatrix/derivatives.py +++ b/cosmicfishpie/fishermatrix/derivatives.py @@ -208,7 +208,7 @@ def der_fwd_4pt(self, fwdi, step): float, numpy.ndarray Numerical derivative using the a 4 point forward stencil """ - der = (-11 * fwdi[0] + 18 * fwdi[1] - 9 * fwdi[2] + 2 * fwdi[3]) / (6 * step**1) + der = (-11 * fwdi[0] + 18 * fwdi[1] - 9 * fwdi[2] + 2 * fwdi[3]) / (6 * step ** 1) return der def derivative_forward_4pt(self): @@ -480,7 +480,7 @@ def derivative_poly(self): stepsize, [obs_mod[step][key][ind] for step in range(len(stepsize))], 4 ) temp.append( - 4 * fit[0] * fidpar**3 * +3 * fit[2] * fidpar**2 + 4 * fit[0] * fidpar ** 3 * +3 * fit[2] * fidpar ** 2 + 2 * fit[3] * fidpar + fit[4] ) diff --git a/cosmicfishpie/utilities/utils.py b/cosmicfishpie/utilities/utils.py index 89f8c18..57b619e 100644 --- a/cosmicfishpie/utilities/utils.py +++ b/cosmicfishpie/utilities/utils.py @@ -80,13 +80,13 @@ def round_decimals_up(number, decimals: int = 2, precision=5): elif number < 1e-1: decimals = 3 - factor = 10**decimals + factor = 10 ** decimals rounded_number = np.ceil(number * factor) / factor else: exponent = math.floor(math.log10(number)) - mantissa = number / (10**exponent) + mantissa = number / (10 ** exponent) rounded_mantissa = math.ceil(mantissa * 10) / 10 - rounded_number = rounded_mantissa * (10**exponent) + rounded_number = rounded_mantissa * (10 ** exponent) return rounded_number @staticmethod From d0576cbf4a989d39c5f3cbfe05c94a5cb85b6311 Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Sun, 1 Dec 2024 14:53:09 +0100 Subject: [PATCH 6/8] merging of other surveys SKAO, DESI, Rubin --- cosmicfishpie/CMBsurvey/CMB_cov.py | 2 +- cosmicfishpie/LSSsurvey/photo_cov.py | 2 +- cosmicfishpie/LSSsurvey/photo_window.py | 2 +- cosmicfishpie/LSSsurvey/spectro_cov.py | 10 ++--- cosmicfishpie/LSSsurvey/spectro_obs.py | 28 ++++++------ cosmicfishpie/analysis/fishconsumer.py | 9 ++-- .../analysis/fisher_plot_analysis.py | 8 ++-- cosmicfishpie/analysis/utilities.py | 2 +- cosmicfishpie/configs/config.py | 45 +++++++++++-------- ...Euclid-Spectroscopic-ISTF-Pessimistic.yaml | 2 +- cosmicfishpie/cosmology/cosmology.py | 24 +++++----- cosmicfishpie/cosmology/nuisance.py | 4 +- cosmicfishpie/fishermatrix/cosmicfish.py | 10 ++--- cosmicfishpie/fishermatrix/derivatives.py | 4 +- cosmicfishpie/utilities/utils.py | 6 +-- tests/conftest.py | 2 + 16 files changed, 86 insertions(+), 74 deletions(-) diff --git a/cosmicfishpie/CMBsurvey/CMB_cov.py b/cosmicfishpie/CMBsurvey/CMB_cov.py index cd25bc5..08d11d6 100644 --- a/cosmicfishpie/CMBsurvey/CMB_cov.py +++ b/cosmicfishpie/CMBsurvey/CMB_cov.py @@ -97,7 +97,7 @@ def getclsnoise(self, cls): ] Bell = [ - np.exp(ang ** 2.0 * noisy_cls["ells"] * (noisy_cls["ells"] + 1) / 2.0) for ang in thetab + np.exp(ang**2.0 * noisy_cls["ells"] * (noisy_cls["ells"] + 1) / 2.0) for ang in thetab ] wtemp = [ diff --git a/cosmicfishpie/LSSsurvey/photo_cov.py b/cosmicfishpie/LSSsurvey/photo_cov.py index c9d57f0..3a54843 100644 --- a/cosmicfishpie/LSSsurvey/photo_cov.py +++ b/cosmicfishpie/LSSsurvey/photo_cov.py @@ -142,7 +142,7 @@ def getclsnoise(self, cls): ) elif obs == "WL": noisy_cls[obs + " " + str(ind) + "x" + obs + " " + str(ind)] += ( - self.ellipt_error ** 2.0 + self.ellipt_error**2.0 ) / self.ngalbin[ind - 1] return noisy_cls diff --git a/cosmicfishpie/LSSsurvey/photo_window.py b/cosmicfishpie/LSSsurvey/photo_window.py index 98f62a7..1672f75 100644 --- a/cosmicfishpie/LSSsurvey/photo_window.py +++ b/cosmicfishpie/LSSsurvey/photo_window.py @@ -78,7 +78,7 @@ def dNdz(self, z): pref = z / self.z0_p expo = z / self.z0 - return pref ** 2 * np.exp(-(expo ** self.ngamma)) + return pref**2 * np.exp(-(expo**self.ngamma)) def n_i(self, z, i): """Function to compute the unnormalized dN/dz(z) with a window picking function applied to it diff --git a/cosmicfishpie/LSSsurvey/spectro_cov.py b/cosmicfishpie/LSSsurvey/spectro_cov.py index 95cbcb5..7934ff2 100644 --- a/cosmicfishpie/LSSsurvey/spectro_cov.py +++ b/cosmicfishpie/LSSsurvey/spectro_cov.py @@ -215,7 +215,7 @@ def veff(self, zi, k, mu): The effective volume for a given wavenumber, angle and redshift """ npobs = self.n_density(zi) * self.pk_obs.observed_Pgg(zi, k, mu) - prefactor = 1 / (8 * (np.pi ** 2)) + prefactor = 1 / (8 * (np.pi**2)) covterm = prefactor * (npobs / (1 + npobs)) ** 2 if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: covterm = np.zeros_like(covterm) @@ -280,7 +280,7 @@ def P_noise_21(self, z, k, mu, temp_dim=True, beam_term=False): beta = self.pk_obs.beta_SD(z, k, mu) else: beta = np.ones_like(k) - noise = pref * cosmo * T_term * (alpha / beta ** 2) + noise = pref * cosmo * T_term * (alpha / beta**2) return noise def veff_II(self, zi, k, mu): @@ -301,7 +301,7 @@ def veff_II(self, zi, k, mu): """ pobs = self.pk_obs.observed_P_ij(zi, k, mu, si="I", sj="I") pnoisy = self.noisy_P_ij(zi, k, mu, si="I", sj="I") - prefactor = 1 / (8 * (np.pi ** 2)) + prefactor = 1 / (8 * (np.pi**2)) covterm = prefactor * (pobs / pnoisy) ** 2 if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: covterm = np.zeros_like(covterm) @@ -330,8 +330,8 @@ def veff_Ig(self, zi, k, mu): pnoisy_Ig = self.noisy_P_ij(zi, k, mu, si="I", sj="g") pnoisy_II = self.noisy_P_ij(zi, k, mu, si="I", sj="I") pnoisy_gg = self.noisy_P_ij(zi, k, mu, si="g", sj="g") - covterm = pobs_Ig ** 2 / (pnoisy_gg * pnoisy_II + pnoisy_Ig * pnoisy_Ig) - prefactor = 1 / (4 * (np.pi ** 2)) + covterm = pobs_Ig**2 / (pnoisy_gg * pnoisy_II + pnoisy_Ig * pnoisy_Ig) + prefactor = 1 / (4 * (np.pi**2)) covterm = prefactor * covterm if zi < self.inter_z_bin_mids[0] or zi > self.inter_z_bin_mids[-1]: covterm = np.zeros_like(covterm) diff --git a/cosmicfishpie/LSSsurvey/spectro_obs.py b/cosmicfishpie/LSSsurvey/spectro_obs.py index c77f8bb..e9326b2 100644 --- a/cosmicfishpie/LSSsurvey/spectro_obs.py +++ b/cosmicfishpie/LSSsurvey/spectro_obs.py @@ -52,10 +52,10 @@ def __init__( fiducial_cosmo : cosmicfishpie.cosmology.cosmology.cosmo_functions, optional An instance of `cosmo_functions` of the fiducial cosmology bias_samples : list, optional - A list of two strings specifying if galaxy clustering, intensity mapping or cross correlation power spectrum should be computed. + A list of two strings specifying if galaxy clustering, intensity mapping or cross correlation power spectrum should be computed. Use "g" for galaxy and "I" for intensity mapping. Default: ["g", "g"] use_bias_funcs : bool, optional - If True will compute the bias function by constructing it from the specification file. + If True will compute the bias function by constructing it from the specification file. If False it will be recomputed from bias parameters. Default: False configuration : object, optional Configuration object containing settings and parameters. If None, uses default config @@ -109,9 +109,9 @@ def __init__( ----- This class can compute: - Galaxy clustering power spectrum - - HI intensity mapping power spectrum + - HI intensity mapping power spectrum - Cross-correlation between galaxy clustering and intensity mapping - + The type of power spectrum is determined by the `bias_samples` parameter: - ["g", "g"]: Galaxy auto-correlation - ["I", "I"]: Intensity mapping auto-correlation @@ -376,7 +376,7 @@ def kper(self, z, k, mu): Observed perpendicular projection of wavevector onto the line of sight with AP-effect corrected for """ - k_per = k * np.sqrt(1 - mu ** 2) * (1 / self.qperpendicular(z)) + k_per = k * np.sqrt(1 - mu**2) * (1 / self.qperpendicular(z)) return k_per def k_units_change(self, k): @@ -471,7 +471,7 @@ def spec_err_z(self, z, k, mu): elif self.dz_type == "z-dependent": spec_dz_err = self.dz_err * (1 + z) err = spec_dz_err * (1 / self.cosmo.Hubble(z)) * self.kpar(z, k, mu) - return np.exp(-(1 / 2) * err ** 2) + return np.exp(-(1 / 2) * err**2) def BAO_term(self, z): """Calculates the BAO term. This is the rescaling of the Fourier volume by the AP-effect @@ -593,9 +593,9 @@ def kaiserTerm(self, z, k, mu, b_i=None, just_rsd=False, bias_sample="g"): fterm = self.cosmo.f_growthrate(z, k, tracer=self.tracer) if not just_rsd: - kaiser = bterm + fterm * mu ** 2 + kaiser = bterm + fterm * mu**2 elif just_rsd: - kaiser = 1 + (fterm / bterm) * mu ** 2 + kaiser = 1 + (fterm / bterm) * mu**2 return kaiser @@ -679,7 +679,7 @@ def sigmavNL(self, zz, mu): f0 = self.P_ThetaTheta_Moments(zz, 0) f1 = self.P_ThetaTheta_Moments(zz, 1) f2 = self.P_ThetaTheta_Moments(zz, 2) - sv = np.sqrt(f0 + 2 * mu ** 2 * f1 + mu ** 2 * f2) + sv = np.sqrt(f0 + 2 * mu**2 * f1 + mu**2 * f2) if self.vary_sigmav: sv *= self.nuisance.vectorized_gcsp_rescale_sigmapv_at_z(zz, sigma_key="sigmav") return sv @@ -714,7 +714,7 @@ def f_mom(k): pp = cosmoF.matpow(zz, self.k_grid).flatten() integrand = pp * ff Int = integrate.trapezoid(integrand, x=self.k_grid) - ptt = (1 / (6 * np.pi ** 2)) * Int + ptt = (1 / (6 * np.pi**2)) * Int return ptt def normalized_pdd(self, z, k): @@ -800,8 +800,8 @@ def dewiggled_pdd(self, z, k, mu): self.p_dd = self.normalized_pdd(z, k) self.p_dd_NW = self.normalized_pnw(z, k) - self.p_dd_DW = self.p_dd * np.exp(-gmudamping * k ** 2) + self.p_dd_NW * ( - 1 - np.exp(-gmudamping * k ** 2) + self.p_dd_DW = self.p_dd * np.exp(-gmudamping * k**2) + self.p_dd_NW * ( + 1 - np.exp(-gmudamping * k**2) ) return self.p_dd_DW @@ -860,7 +860,7 @@ def observed_Pgg(self, z, k, mu, b_i=None): lorentzFoG = self.FingersOfGod(z, k, mu, mode="Lorentz") p_dd_DW = self.dewiggled_pdd(z, k, mu) - pgg_obs = baoterm * (kaiser ** 2) * p_dd_DW * lorentzFoG * (error_z ** 2) + extra_shotnoise + pgg_obs = baoterm * (kaiser**2) * p_dd_DW * lorentzFoG * (error_z**2) + extra_shotnoise tend = time() upt.time_print( @@ -940,7 +940,7 @@ def beta_SD(self, z, k, mu): tol = 1.0e-12 k = np.atleast_1d(k) mu = np.atleast_1d(mu) - expo = k ** 2 * (1 - mu ** 2) * self.fiducialcosmo.comoving(z) ** 2 * self.theta_b(z) ** 2 + expo = k**2 * (1 - mu**2) * self.fiducialcosmo.comoving(z) ** 2 * self.theta_b(z) ** 2 bet = np.exp(-expo / (16.0 * np.log(2.0))) bet[np.abs(bet) < tol] = tol return bet diff --git a/cosmicfishpie/analysis/fishconsumer.py b/cosmicfishpie/analysis/fishconsumer.py index 85b436f..3e952fa 100644 --- a/cosmicfishpie/analysis/fishconsumer.py +++ b/cosmicfishpie/analysis/fishconsumer.py @@ -101,7 +101,7 @@ def display_colors(colors, figsize=(6, 6)): # rotation_angle = angle x1, y1 = wedges[i].center x2, y2 = np.cos(np.deg2rad(angle)), np.sin(np.deg2rad(angle)) - dx, dy = 1.2 * np.array([x2, y2]) / np.sqrt(x2 ** 2 + y2 ** 2) + dx, dy = 1.2 * np.array([x2, y2]) / np.sqrt(x2**2 + y2**2) ax.annotate( color, xy=(x1, y1), @@ -627,7 +627,8 @@ def prepare_settings_plot( def chainfishplot( - return_dictionary, **cckwargs, + return_dictionary, + **cckwargs, ): """ Chain fish plot function @@ -787,7 +788,7 @@ def simple_fisher_plot( output_file="fisher_plot.pdf", ): """Create a triangle plot from Fisher matrices using ChainConsumer. - + Parameters ---------- fisher_list : list @@ -802,7 +803,7 @@ def simple_fisher_plot( Whether to save the plot to file (default: False) output_file : str, optional Filename for saving the plot (default: 'fisher_plot.pdf') - + Returns ------- matplotlib.figure.Figure diff --git a/cosmicfishpie/analysis/fisher_plot_analysis.py b/cosmicfishpie/analysis/fisher_plot_analysis.py index 7486886..17c98f4 100644 --- a/cosmicfishpie/analysis/fisher_plot_analysis.py +++ b/cosmicfishpie/analysis/fisher_plot_analysis.py @@ -663,14 +663,14 @@ def compute_gaussian( if normalized: y_points = np.array( [ - np.exp(-((x - fiducial) ** 2) / (2.0 * sigma ** 2)) + np.exp(-((x - fiducial) ** 2) / (2.0 * sigma**2)) / (sigma * np.sqrt(2.0 * math.pi)) for x in x_points ] ) else: y_points = np.array( - [np.exp(-((x - fiducial) ** 2) / (2.0 * sigma ** 2)) for x in x_points] + [np.exp(-((x - fiducial) ** 2) / (2.0 * sigma**2)) for x in x_points] ) dict_names[name] = [x_points, y_points, [fiducial, sigma]] @@ -749,11 +749,11 @@ def compute_ellipse( # compute the ellipse coefficients: coeff_a = confidence_coefficient * math.sqrt( (sigma_x + sigma_y) / 2.0 - + math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy ** 2) + + math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy**2) ) coeff_b = confidence_coefficient * math.sqrt( (sigma_x + sigma_y) / 2.0 - - math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy ** 2) + - math.sqrt((sigma_x - sigma_y) ** 2 / 4.0 + sigma_xy**2) ) theta_0 = math.atan2((2.0 * sigma_xy), (sigma_x - sigma_y)) / 2.0 # generate the ellipses diff --git a/cosmicfishpie/analysis/utilities.py b/cosmicfishpie/analysis/utilities.py index 93d8f5f..dafae55 100644 --- a/cosmicfishpie/analysis/utilities.py +++ b/cosmicfishpie/analysis/utilities.py @@ -43,7 +43,7 @@ def num_to_mant_exp(num): exponent = math.floor(math.log10(abs(num))) except ValueError: # Case of log10(0) return (0, 0) # Convention: 0 = 0*10^0 - mantissa = num / 10 ** exponent + mantissa = num / 10**exponent return (mantissa, int(exponent)) diff --git a/cosmicfishpie/configs/config.py b/cosmicfishpie/configs/config.py index df40a60..8bd8d51 100644 --- a/cosmicfishpie/configs/config.py +++ b/cosmicfishpie/configs/config.py @@ -208,7 +208,10 @@ def init( # Set defaults if not contained previously in options settings.setdefault( "specs_dir_default", - os.path.join(os.path.dirname(os.path.realpath(__file__)), "default_survey_specifications",), + os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "default_survey_specifications", + ), ) settings.setdefault("specs_dir", settings["specs_dir_default"]) settings.setdefault("survey_name", surveyName) @@ -231,6 +234,8 @@ def init( settings.setdefault("Pshot_nuisance_fiducial", 0.0) settings.setdefault("pivot_z_IA", 0.0) settings.setdefault("accuracy", 1) + settings.setdefault("spectro_Pk_k_samples", 1025) + settings.setdefault("spectro_Pk_mu_samples", 17) settings.setdefault("feedback", 2) settings.setdefault("activateMG", False) settings.setdefault("external_activateMG", False) @@ -366,11 +371,11 @@ def ngal_per_bin(ngal_sqarmin, zbins): def create_ph_dict(foldername, filename): photo_dict = dict() - if filename == False: + if not filename: upt.time_print( feedback_level=feed_lvl, min_level=1, - text=f"-> No photo survey passed, returning empty dict", + text="-> No photo survey passed, returning empty dict", ) return photo_dict try: @@ -405,7 +410,7 @@ def create_ph_dict(foldername, filename): def create_sp_dict(foldername, filename, type="spectro"): spec_dict = dict() - if filename == False: + if not filename: upt.time_print( feedback_level=feed_lvl, min_level=1, @@ -471,7 +476,7 @@ def create_sp_dict(foldername, filename, type="spectro"): upt.time_print( feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNameRadioIM}" ) - if spectroTaken == False: + if not spectroTaken: surveyNameSpectro = settings.get("survey_name_spectro") specificationsf4 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) specificationsf.update(specificationsf4) @@ -479,7 +484,7 @@ def create_sp_dict(foldername, filename, type="spectro"): upt.time_print( feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNameSpectro}" ) - if photoTaken == False: + if not photoTaken: surveyNamePhoto = settings.get("survey_name_photo") specificationsf5 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) specificationsf.update(specificationsf5) @@ -488,7 +493,7 @@ def create_sp_dict(foldername, filename, type="spectro"): feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNamePhoto}" ) if "Rubin" in surveyName: - if photoTaken == False: + if not photoTaken: surveyNamePhoto = settings.get("survey_name_photo") specificationsf6 = create_ph_dict(settings["specs_dir"], surveyNamePhoto) specificationsf.update(specificationsf6) @@ -497,7 +502,7 @@ def create_sp_dict(foldername, filename, type="spectro"): feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: {surveyNamePhoto}" ) if "DESI" in surveyName: - if spectroTaken == False: + if not spectroTaken: surveyNameSpectro = settings.get("survey_name_spectro") specificationsf7 = create_sp_dict(settings["specs_dir"], surveyNameSpectro) specificationsf.update(specificationsf7) @@ -510,7 +515,7 @@ def create_sp_dict(foldername, filename, type="spectro"): parsed_yaml_file = yaml.load(yaml_file, Loader=yaml.FullLoader) specificationsfPlanck = parsed_yaml_file["specifications"] specificationsf.update(specificationsfPlanck) - upt.time_print(feedback_level=feed_lvl, min_level=1, text=f"-> Survey loaded: Planck") + upt.time_print(feedback_level=feed_lvl, min_level=1, text="-> Survey loaded: Planck") if surveyName not in available_survey_names: print("Survey name passed: ", surveyName) print( @@ -598,7 +603,9 @@ def create_sp_dict(foldername, filename, type="spectro"): global fiducialcosmo upt.time_print( - feedback_level=feed_lvl, min_level=1, text="-> Computing cosmology at the fiducial point", + feedback_level=feed_lvl, + min_level=1, + text="-> Computing cosmology at the fiducial point", ) tcosmo1 = time() fiducialcosmo = cosmology.cosmo_functions(fiducialparams, input_type) @@ -722,20 +729,22 @@ def create_sp_dict(foldername, filename, type="spectro"): IMbiasparams[key] = bias_prmod[key] # Set the default free parameters for the spectro nuisance parameters + default_eps_gc_nuis = settings["eps_gal_nuispars"] + default_eps_gc_nonlin = settings["eps_gal_nonlinpars"] + if "GCsp" in obs: + for key in Spectrobiasparams: + freeparams.setdefault(key, default_eps_gc_nuis) + if "IM" in obs: + for key in IMbiasparams: + freeparams.setdefault(key, default_eps_gc_nuis) if "GCsp" in obs or "IM" in obs: - default_eps_gc_nuis = settings["eps_gal_nuispars"] - default_eps_gc_nonlin = settings["eps_gal_nonlinpars"] for key in PShotparams: freeparams.setdefault(key, default_eps_gc_nuis) for key in Spectrononlinearparams: freeparams.setdefault(key, default_eps_gc_nonlin) # Only add the free parameters that are not already in the dictionary - if "GCsp" in obs: - for key in Spectrobiasparams: - freeparams.setdefault(key, default_eps_gc_nuis) - if "IM" in obs: - for key in IMbiasparams: - freeparams.setdefault(key, default_eps_gc_nuis) + upt.debug_print("Final dict of free parameters in config.py:") + upt.debug_print(freeparams) global latex_names latex_names_def = { diff --git a/cosmicfishpie/configs/default_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic.yaml b/cosmicfishpie/configs/default_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic.yaml index efb1be0..bd99ca5 100644 --- a/cosmicfishpie/configs/default_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic.yaml +++ b/cosmicfishpie/configs/default_survey_specifications/Euclid-Spectroscopic-ISTF-Pessimistic.yaml @@ -15,7 +15,7 @@ specifications: 3: 1410.0 4: 940.97 sp_bias_sample: 'g' - sp_bias_model: 'linear_log' + sp_bias_model: 'linear_log' #'linear_log' sp_bias_root: 'lnb' sp_bias_parametrization: linear: diff --git a/cosmicfishpie/cosmology/cosmology.py b/cosmicfishpie/cosmology/cosmology.py index f10fcd2..e82d49e 100644 --- a/cosmicfishpie/cosmology/cosmology.py +++ b/cosmicfishpie/cosmology/cosmology.py @@ -301,10 +301,10 @@ def compute_sigma8(z_range, pk_interpolator, h_value, k_range): 9 * (k * R * np.cos(k * R) - np.sin(k * R)) ** 2 * pk_z[i] - / k ** 4 - / R ** 6 + / k**4 + / R**6 / 2 - / np.pi ** 2 + / np.pi**2 ) sigma_z[i] = np.sqrt(integrate.trapezoid(integrand, k)) @@ -426,7 +426,7 @@ def changebasis_camb(self, cosmopars, camb): g_factor = fidNeff / 3 neutrino_mass_fac = boltzmann_code.hardcoded_neutrino_mass_fac - h2 = self.h_now ** 2 + h2 = self.h_now**2 if "mnu" in cambpars: Onu = cambpars["mnu"] / neutrino_mass_fac * (g_factor) ** 0.75 / h2 @@ -611,11 +611,11 @@ def camb_results(self, camb): ) Pk_cb_nl = ( 1 - / f_cb ** 2 + / f_cb**2 * ( Pk_nl.P(self.results.zgrid, self.results.kgrid) - 2 * Pk_cross_l.P(self.results.zgrid, self.results.kgrid) * f_cb * f_nu - - Pk_nunu_l.P(self.results.zgrid, self.results.kgrid) * f_nu ** 2 + - Pk_nunu_l.P(self.results.zgrid, self.results.kgrid) * f_nu**2 ) ) self.results.Pk_cb_nl = RectBivariateSpline( @@ -756,7 +756,7 @@ def changebasis_class(self, cosmopars): if "mnu" in classpars: classpars["T_ncdm"] = (4.0 / 11.0) ** (1.0 / 3.0) * g_factor ** (1.0 / 4.0) classpars["Omega_ncdm"] = ( - classpars["mnu"] * g_factor ** (0.75) / neutrino_mass_fac / h ** 2 + classpars["mnu"] * g_factor ** (0.75) / neutrino_mass_fac / h**2 ) classpars.pop("mnu") # classpars['m_ncdm'] = classpars.pop('mnu') @@ -844,13 +844,13 @@ def class_results(self, Class): pm = classres.get_primordial() pk_prim = ( UnivariateSpline(pm["k [1/Mpc]"], pm["P_scalar(k)"])(k) - * (2.0 * np.pi ** 2) + * (2.0 * np.pi**2) / np.power(k, 3) ) pk_cnu = T_nu * T_cb * pk_prim[:, None] pk_nunu = T_nu * T_nu * pk_prim[:, None] - Pk_cb_nl = 1.0 / f_cb ** 2 * (Pk_nl - 2 * pk_cnu * f_nu * f_cb - pk_nunu * f_nu * f_nu) + Pk_cb_nl = 1.0 / f_cb**2 * (Pk_nl - 2 * pk_cnu * f_nu * f_cb - pk_nunu * f_nu * f_nu) self.results.Pk_cb_nl = RectBivariateSpline( z[::-1], k, (np.flip(Pk_cb_nl, axis=1)).transpose() @@ -952,9 +952,9 @@ def changebasis_symb(self, cosmopars): # ["sigma8", "As", "logAs", "10^9As", "ln_A_s_1e10"] try: if "As" in symbpars: - symbpars["10^9As"] = 10 ** 9 * symbpars.pop("As") + symbpars["10^9As"] = 10**9 * symbpars.pop("As") if "logAs" in symbpars: - symbpars["10^9As"] = 10 ** 9 * (np.exp(symbpars.pop("logAs")) * 1.0e-10) + symbpars["10^9As"] = 10**9 * (np.exp(symbpars.pop("logAs")) * 1.0e-10) try: As_value = symbpars.get("10^9As") upr.debug_print("DEBUG: symbpars['10^9As'] = ", As_value) @@ -1096,7 +1096,7 @@ def symbolic_results(self): extrapolate=self.extrapolate, ) # symbfit plin_emulated returns P_l(k,z=0) in 1/Mpc^3, requests kgrid in h/Mpc - Pk_at_z = (D_kz ** 2) * self.results.Pk_l_0 + Pk_at_z = (D_kz**2) * self.results.Pk_l_0 self.results.Pk_l = RectBivariateSpline( self.zgrid, self.kgrid_1Mpc, Pk_at_z ) # P_l(k,z) in 1/Mpc^3 diff --git a/cosmicfishpie/cosmology/nuisance.py b/cosmicfishpie/cosmology/nuisance.py index 232f7e5..7195e51 100644 --- a/cosmicfishpie/cosmology/nuisance.py +++ b/cosmicfishpie/cosmology/nuisance.py @@ -296,7 +296,7 @@ def gcsp_bias_kscale(self, k, z=None): default_A1 = 0.0 default_A2 = 0.0 try: - bterm_k = (1 + k ** 2 * self.Spectrobiasparams.get("A2", default_A2)) / ( + bterm_k = (1 + k**2 * self.Spectrobiasparams.get("A2", default_A2)) / ( 1 + k * self.Spectrobiasparams.get("A1", default_A1) ) except KeyError as ke: @@ -386,7 +386,7 @@ def IM_THI_noise(self): Returns ------- scipy.interpolate.UnivariateSpline - Interpolation function that takes redshift as input and returns the + Interpolation function that takes redshift as input and returns the corresponding system noise temperature value. Notes diff --git a/cosmicfishpie/fishermatrix/cosmicfish.py b/cosmicfishpie/fishermatrix/cosmicfish.py index 9fb41bc..2389f21 100644 --- a/cosmicfishpie/fishermatrix/cosmicfish.py +++ b/cosmicfishpie/fishermatrix/cosmicfish.py @@ -282,7 +282,7 @@ def compute(self, max_z_bins=None): ) self.zmids = self.pk_cov.inter_z_bin_mids nbins = len(self.zmids) - if max_z_bins is not None and type(max_z_bins) == int: + if max_z_bins is not None and isinstance(max_z_bins, int): cutnbins = max_z_bins self.eliminate_zbinned_freepars(cutnbins) self.zmids = self.zmids[0:cutnbins] @@ -371,8 +371,8 @@ def set_pk_settings(self): k_units_factor = self.fiducialcosmopars["h"] self.kmin_fish = self.specs["kmin_GCsp"] * k_units_factor self.kmax_fish = self.specs["kmax_GCsp"] * k_units_factor - self.Pk_ksamp = 513 * self.settings["accuracy"] - self.Pk_musamp = 9 * self.settings["accuracy"] + self.Pk_ksamp = self.settings["spectro_Pk_k_samples"] + self.Pk_musamp = self.settings["spectro_Pk_mu_samples"] self.Pk_kgrid = np.linspace(self.kmin_fish, self.kmax_fish, self.Pk_ksamp) self.Pk_mugrid = np.linspace(0.0, 1.0, self.Pk_musamp) self.Pk_kmesh, self.Pk_mumesh = np.meshgrid(self.Pk_kgrid, self.Pk_mugrid) @@ -535,7 +535,7 @@ def fish_integrand(self, zi, k, mu, pi, pj): # zmid = self.pk_cov.global_z_bin_mids[zi] dPdpi = self.derivs_dict[pi][zi] dPdpj = self.derivs_dict[pj][zi] - intg = k ** 2 * pref * volsurv * self.veff_arr[zi] * dPdpi * dPdpj + intg = k**2 * pref * volsurv * self.veff_arr[zi] * dPdpi * dPdpj return intg def photo_LSS_fishermatrix(self, noisy_cls=None, covmat=None, derivs=None, lss_obj=None): @@ -819,7 +819,7 @@ def eliminate_zbinned_freepars(self, nmax): ibin = int(keysplit[1]) except ValueError: continue - if type(ibin) == int and ibin > nmax: + if isinstance(ibin, int) and ibin > nmax: removedpar = self.freeparams.pop(key) upt.time_print( feedback_level=self.feed_lvl, diff --git a/cosmicfishpie/fishermatrix/derivatives.py b/cosmicfishpie/fishermatrix/derivatives.py index 1bacd11..9a014c9 100644 --- a/cosmicfishpie/fishermatrix/derivatives.py +++ b/cosmicfishpie/fishermatrix/derivatives.py @@ -208,7 +208,7 @@ def der_fwd_4pt(self, fwdi, step): float, numpy.ndarray Numerical derivative using the a 4 point forward stencil """ - der = (-11 * fwdi[0] + 18 * fwdi[1] - 9 * fwdi[2] + 2 * fwdi[3]) / (6 * step ** 1) + der = (-11 * fwdi[0] + 18 * fwdi[1] - 9 * fwdi[2] + 2 * fwdi[3]) / (6 * step**1) return der def derivative_forward_4pt(self): @@ -480,7 +480,7 @@ def derivative_poly(self): stepsize, [obs_mod[step][key][ind] for step in range(len(stepsize))], 4 ) temp.append( - 4 * fit[0] * fidpar ** 3 * +3 * fit[2] * fidpar ** 2 + 4 * fit[0] * fidpar**3 * +3 * fit[2] * fidpar**2 + 2 * fit[3] * fidpar + fit[4] ) diff --git a/cosmicfishpie/utilities/utils.py b/cosmicfishpie/utilities/utils.py index 57b619e..89f8c18 100644 --- a/cosmicfishpie/utilities/utils.py +++ b/cosmicfishpie/utilities/utils.py @@ -80,13 +80,13 @@ def round_decimals_up(number, decimals: int = 2, precision=5): elif number < 1e-1: decimals = 3 - factor = 10 ** decimals + factor = 10**decimals rounded_number = np.ceil(number * factor) / factor else: exponent = math.floor(math.log10(number)) - mantissa = number / (10 ** exponent) + mantissa = number / (10**exponent) rounded_mantissa = math.ceil(mantissa * 10) / 10 - rounded_number = rounded_mantissa * (10 ** exponent) + rounded_number = rounded_mantissa * (10**exponent) return rounded_number @staticmethod diff --git a/tests/conftest.py b/tests/conftest.py index 5ef6528..dc8eebf 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,9 @@ import pytest from cosmicfishpie.fishermatrix import cosmicfish as cff +from cosmicfishpie.utilities.utils import printing as upt +upt.debug = True code_to_use = "symbolic" From 8930e6e3b6c0900f9d842fc808865ad1bc857128 Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Sun, 1 Dec 2024 14:54:55 +0100 Subject: [PATCH 7/8] changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 03b1999..f6b5f73 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -31,3 +31,4 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 1.1.3 : New split up demonstration notebooks 1.1.4 : Coverage reports with Codecov 1.2.0 : Big update of configuration, specification yamls and nuisance parameter interface. No backwards compatibility of yamls! +1.2.1 : Updating configs of other surveys: SKAO, DESI, LSST to work in new config file structure From 49d720c59709ede1d6b4fd4fac4760ff6722a843 Mon Sep 17 00:00:00 2001 From: Santiago Casas Date: Sun, 1 Dec 2024 15:02:57 +0100 Subject: [PATCH 8/8] fix docstring --- cosmicfishpie/cosmology/nuisance.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/cosmicfishpie/cosmology/nuisance.py b/cosmicfishpie/cosmology/nuisance.py index 7195e51..26ac9a5 100644 --- a/cosmicfishpie/cosmology/nuisance.py +++ b/cosmicfishpie/cosmology/nuisance.py @@ -378,25 +378,25 @@ def IM_bias_fitting(self, z): return bb def IM_THI_noise(self): - """Get the system noise temperature interpolation function for Intensity Mapping. + """Create interpolation function for HI intensity mapping system noise temperature. - This function creates an interpolation of the system noise temperature (T_sys) - as a function of redshift for HI intensity mapping observations. + Creates a spline interpolation of the system noise temperature (T_sys) as a + function of redshift for HI intensity mapping observations. The noise temperature + data is read from the survey specifications. Returns ------- - scipy.interpolate.UnivariateSpline + UnivariateSpline Interpolation function that takes redshift as input and returns the - corresponding system noise temperature value. + corresponding system noise temperature in Kelvin. Notes ----- - The function reads the system noise data from the survey specifications, - which should contain: - - z_vals_THI : array-like - Redshift values where the noise temperature is defined - - THI_sys_noise : array-like - System noise temperature values corresponding to the redshift points + The survey specifications must contain a 'THI_sys_noise' dictionary with: + - 'z_vals_THI' : array-like + Redshift values where noise temperature is defined + - 'THI_sys_noise' : array-like + System noise temperature values in Kelvin corresponding to each redshift """ THI_sys = self.specs["THI_sys_noise"] z_vals_THI = THI_sys["z_vals_THI"]