Same Update to master

soliket/bias/bias.py

@@ -26,8 +26,6 @@
 function (have a look at the linear bias model for ideas).
 """
 
-from typing import Optional
-
 import numpy as np
 from cobaya.theory import Theory
 
@@ -72,10 +70,9 @@ def must_provide(self, **requirements):
         return needs
 
     def _get_Pk_mm(self):
-        for pair in self._var_pairs:
-            self.k, self.z, Pk_mm = \
-                self.provider.get_Pk_grid(var_pair=pair, nonlinear=self.nonlinear)
-
+        self.k, self.z, Pk_mm = \
+            self.provider.get_Pk_grid(var_pair=list(self._var_pairs)[0],
+                                      nonlinear=self.nonlinear)
         return Pk_mm
 
     def get_Pk_gg_grid(self) -> dict:
@@ -93,7 +90,7 @@ class Linear_bias(Bias):
     """
 
     def calculate(self, state: dict, want_derived: bool = True,
-                  **params_values_dict) -> Optional[bool]:
+                  **params_values_dict):
         Pk_mm = self._get_Pk_mm()
 
         state["Pk_gg_grid"] = params_values_dict["b_lin"] ** 2. * Pk_mm

soliket/cash/cash.py

@@ -1,11 +1,12 @@
 from typing import Optional
-
 import numpy as np
 from cobaya.likelihood import Likelihood
-
 from .cash_data import CashCData
 
 
+# Likelihood for independent Poisson-distributed data
+# (here called Cash-C, see https://arxiv.org/abs/1912.05444)
+
 class CashCLikelihood(Likelihood):
     name: str = "Cash-C"
     datapath = Optional[str]
@@ -17,7 +18,7 @@ def initialize(self):
 
     def _get_data(self):
         data = np.loadtxt(self.datapath, unpack=False)
-        N = data[:, -1] # assume data stored like column_stack([z, q, N])
+        N = data[:, -1]  # assume data stored like column_stack([z, q, N])
         x = data[:, :-1]
         return x, N
 

soliket/ccl/ccl.py

@@ -156,7 +156,7 @@ def get_can_support_params(self) -> Sequence[str]:
         return []
 
     def calculate(self, state: dict, want_derived: bool = True,
-                  **params_values_dict) -> bool:
+                  **params_values_dict):
         # calculate the general CCL cosmo object which likelihoods can then use to get
         # what they need (likelihoods should cache results appropriately)
         # get our requirements from self.provider

soliket/clusters/clusters.py

@@ -18,7 +18,6 @@
 p
 """
 import os
-
 import numpy as np
 import pandas as pd
 from scipy.interpolate import interp1d
@@ -27,7 +26,7 @@
 from soliket.poisson import PoissonLikelihood
 
 from .survey import SurveyData
-from .sz_utils import szutils
+from .sz_utils import szutils, trapezoid
 from cobaya import LoggedError
 
 C_KM_S = 2.99792e5
@@ -87,15 +86,15 @@ def get_requirements(self):
             # "CCL": {"methods": {"sz_model": self._get_sz_model}, "kmax": 10},
         }
 
-    def _get_sz_model(self, cosmo):
-        model = SZModel()
-        model.hmf = self.ccl.halos.MassFuncTinker08(cosmo, mass_def=self.mdef)
-        model.hmb = self.ccl.halos.HaloBiasTinker10(
-            cosmo, mass_def=self.mdef, mass_def_strict=False
-        )
-        model.hmc = self.ccl.halos.HMCalculator(cosmo, model.hmf, model.hmb, self.mdef)
-        # model.szk = SZTracer(cosmo)
-        return model
+    # def _get_sz_model(self, cosmo):
+    #     model = SZModel()
+    #     model.hmf = self.ccl.halos.MassFuncTinker08(cosmo, mass_def=self.mdef)
+    #     model.hmb = self.ccl.halos.HaloBiasTinker10(
+    #         cosmo, mass_def=self.mdef, mass_def_strict=False
+    #     )
+    #     model.hmc = self.ccl.halos.HMCalculator(cosmo, model.hmf, model.hmb, self.mdef)
+    #     # model.szk = SZTracer(cosmo)
+    #     return model
 
     def _get_catalog(self):
         self.survey = SurveyData(
@@ -202,7 +201,7 @@ def Prob_per_cluster(z, tsz_signal, tsz_signal_err):
 
             dn_dzdm = 10 ** np.squeeze(dn_dzdm_interp((np.log10(HMF.M), c_z))) * h ** 4.0
 
-            ans = np.trapz(dn_dzdm * Pfunc_ind, dx=np.diff(HMF.M, axis=0), axis=0)
+            ans = trapezoid(dn_dzdm * Pfunc_ind, dx=np.diff(HMF.M, axis=0), axis=0)
             return ans
 
         return Prob_per_cluster
@@ -241,11 +240,11 @@ def _get_n_expected(self, **kwargs):
 
         for Yt, frac in zip(self.survey.Ythresh, self.survey.frac_of_survey):
             Pfunc = self.szutils.PfuncY(Yt, HMF.M, z_arr, param_vals, Ez_fn, DA_fn)
-            N_z = np.trapz(
+            N_z = trapezoid(
                 dn_dzdm * Pfunc, dx=np.diff(HMF.M[:, None] / h, axis=0), axis=0
             )
             Ntot += (
-                    np.trapz(N_z * dVdz, x=z_arr)
+                    trapezoid(N_z * dVdz, x=z_arr)
                     * 4.0
                     * np.pi
                     * self.survey.fskytotal
@@ -269,9 +268,9 @@ def _test_n_tot(self, **kwargs):
         dn_dzdm = HMF.dn_dM(HMF.M, 500.0) * h ** 4.0  # getting rid of hs
         # Test Mass function against Nemo.
         Pfunc = 1.0
-        N_z = np.trapz(dn_dzdm * Pfunc, dx=np.diff(HMF.M[:, None] / h, axis=0), axis=0)
+        N_z = trapezoid(dn_dzdm * Pfunc, dx=np.diff(HMF.M[:, None] / h, axis=0), axis=0)
         Ntot = (
-                np.trapz(N_z * dVdz, x=z_arr)
+                trapezoid(N_z * dVdz, x=z_arr)
                 * 4.0
                 * np.pi
                 * (600.0 / (4 * np.pi * (180 / np.pi) ** 2))

soliket/clusters/sz_utils.py

@@ -8,6 +8,10 @@
 """
 
 import numpy as np
+try:
+    from numpy import trapezoid
+except ImportError:
+    from numpy import trapz as trapezoid
 from scipy import interpolate
 
 # from nemo import signals
@@ -16,8 +20,6 @@
                                k_Boltzmann)
 
 # from astropy.cosmology import FlatLambdaCDM
-
-
 # from .clusters import C_KM_S as C_in_kms
 
 rho_crit0H100 = (3. / (8. * np.pi) * (100. * 1.e5) ** 2.) \
@@ -112,7 +114,7 @@ def P_of_gt_SN(self, LgY, MM, zz, Ynoise, param_vals, Ez_fn, Da_fn):
 
         P_Y = np.nan_to_num(self.P_Yo_vec(LgYa2, MM, zz, param_vals, Ez_fn, Da_fn))
 
-        ans = np.trapz(P_Y * sig_thresh, x=LgY, axis=2) * np.log(10)
+        ans = trapezoid(P_Y * sig_thresh, x=LgY, axis=2) * np.log(10)
         return ans
 
     def PfuncY(self, YNoise, M, z_arr, param_vals, Ez_fn, Da_fn):
@@ -126,13 +128,12 @@ def PfuncY(self, YNoise, M, z_arr, param_vals, Ez_fn, Da_fn):
 
     def P_of_Y_per(self, LgY, MM, zz, Y_c, Y_err, param_vals):
         P_Y_sig = np.outer(np.ones(len(MM)), self.Y_prob(Y_c, LgY, Y_err))
-        LgYa = np.outer(np.ones(len(MM)), LgY)
 
         LgYa = np.outer(np.ones([MM.shape[0], MM.shape[1]]), LgY)
         LgYa2 = np.reshape(LgYa, (MM.shape[0], MM.shape[1], len(LgY)))
 
         P_Y = np.nan_to_num(self.P_Yo(LgYa2, MM, zz, param_vals))
-        ans = np.trapz(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=1) * np.log(10)
+        ans = trapezoid(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=1) * np.log(10)
 
         return ans
 
@@ -148,7 +149,7 @@ def Pfunc_per(self, MM, zz, Y_c, Y_err, param_vals, Ez_fn, Da_fn):
 
         P_Y_sig = self.Y_prob(Y_c, LgY, Y_err)
         P_Y = np.nan_to_num(self.P_Yo(LgYa, MM, zz, param_vals, Ez_fn, Da_fn))
-        ans = np.trapz(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=1)
+        ans = trapezoid(P_Y * P_Y_sig, LgY, np.diff(LgY), axis=1)
 
         return ans
 
@@ -171,7 +172,7 @@ def Pfunc_per_parallel(self, Marr, zarr, Y_c, Y_err, param_vals, Ez_fn, Da_fn):
         P_Y_sig = self.Y_prob(Y_c, self.LgY, Y_err)
         P_Y = np.nan_to_num(self.P_Yo(self.LgY, Marr, zarr, param_vals, Ez_fn, Da_fn))
 
-        ans = np.trapz(P_Y * P_Y_sig, x=self.LgY, axis=2)
+        ans = trapezoid(P_Y * P_Y_sig, x=self.LgY, axis=2)
 
         return ans
 

soliket/cross_correlation/cross_correlation.py

@@ -6,6 +6,10 @@
 """
 
 import numpy as np
+try:
+    from numpy import trapezoid
+except ImportError:
+    from numpy import trapz as trapezoid
 import sacc
 from cobaya.log import LoggedError
 
@@ -58,7 +62,7 @@ def _get_nz(self, z, tracer, tracer_name, **params_values):
             bias = params_values['{}_deltaz'.format(tracer_name)]
             nz_biased = tracer.get_dndz(z - bias)
 
-        # nz_biased /= np.trapz(nz_biased, z)
+        # nz_biased /= np.trapezoid(nz_biased, z)
 
         return nz_biased
 
@@ -198,7 +202,7 @@ def _get_theory(self, **params_values):
                 elif self.ia_mode == 'nla':
                     A_IA = params_values['A_IA']
                     eta_IA = params_values['eta_IA']
-                    z0_IA = np.trapz(z_tracer1 * nz_tracer1)
+                    z0_IA = trapezoid(z_tracer1 * nz_tracer1)
 
                     ia_z = (z_tracer1, A_IA * ((1 + z_tracer1) / (1 + z0_IA)) ** eta_IA)
                 elif self.ia_mode == 'nla-perbin':
@@ -238,7 +242,7 @@ def _get_theory(self, **params_values):
                 elif self.ia_mode == 'nla':
                     A_IA = params_values['A_IA']
                     eta_IA = params_values['eta_IA']
-                    z0_IA = np.trapz(z_tracer2 * nz_tracer2)
+                    z0_IA = trapezoid(z_tracer2 * nz_tracer2)
 
                     ia_z = (z_tracer2, A_IA * ((1 + z_tracer2) / (1 + z0_IA)) ** eta_IA)
                 elif self.ia_mode == 'nla-perbin':

soliket/gaussian/gaussian.py

@@ -73,12 +73,12 @@ def initialize(self):
 
         self.log.info('Initialized.')
 
-    def initialize_with_provider(self, provider): # pragma: no cover
+    def initialize_with_provider(self, provider):  # pragma: no cover
         for like in self.likelihoods:
             like.initialize_with_provider(provider)
         # super().initialize_with_provider(provider)
 
-    def get_helper_theories(self): # pragma: no cover
+    def get_helper_theories(self):  # pragma: no cover
         helpers = {}
         for like in self.likelihoods:
             helpers.update(like.get_helper_theories())

soliket/gaussian/gaussian_data.py

@@ -1,53 +1,51 @@
+from typing import Optional, Sequence
 import numpy as np
-
-from scipy.linalg import LinAlgError, cholesky
-
-try:
-    import holoviews as hv
-except ImportError:
-    pass
-
-
-def multivariate_normal_logpdf(theory, data, cov, inv_cov, log_det):
-    const = np.log(2 * np.pi) * (-len(data) / 2) + log_det * (-1 / 2)
-    delta = data - theory
-    #print(const,delta,np.dot(delta, inv_cov.dot(delta)))
-    return -0.5 * np.dot(delta, inv_cov.dot(delta)) + const
+from cobaya.likelihoods.base_classes import _fast_chi_square
 
 
 class GaussianData:
-    """Named multivariate gaussian data
     """
+     Named multivariate gaussian data
+    """
+    name: str  # name identifier for the data
+    x: Sequence  # labels for each data point
+    y: np.ndarray  # data point values
+    cov: np.ndarray  # covariance matrix
+    inv_cov: np.ndarray  # inverse covariance matrix
+    ncovsims: Optional[int]  # number of simulations used to estimate covariance
+
+    _fast_chi_squared = _fast_chi_square()
 
-    def __init__(self, name, x, y, cov, ncovsims=None):
+    def __init__(self, name, x: Sequence, y: Sequence[float], cov: np.ndarray,
+                 ncovsims: Optional[int] = None):
 
         self.name = str(name)
         self.ncovsims = ncovsims
 
         if not (len(x) == len(y) and cov.shape == (len(x), len(x))):
-            raise ValueError(f"Incompatible shapes! x={x.shape}, y={y.shape}, \
+            raise ValueError(f"Incompatible shapes! x={len(x)}, y={len(y)}, \
                                cov={cov.shape}")
 
         self.x = x
-        self.y = y
+        self.y = np.ascontiguousarray(y)
         self.cov = cov
-        try:
-            self.cholesky = cholesky(cov)
-        except LinAlgError:
-            raise ValueError("Covariance is not SPD!")
-        if ncovsims is None:
-            self.inv_cov = np.linalg.inv(self.cov)
-        else:
+        self.eigenevalues = np.linalg.eigvalsh(cov)
+        if self.eigenevalues.min() <= 0:
+            raise ValueError("Covariance is not positive definite!")
+
+        self.inv_cov = np.linalg.inv(self.cov)
+        if ncovsims is not None:
             hartlap_factor = (self.ncovsims - len(x) - 2) / (self.ncovsims - 1)
-            self.inv_cov = hartlap_factor * np.linalg.inv(self.cov)
-        self.log_det = np.linalg.slogdet(self.cov)[1]
+            self.inv_cov *= hartlap_factor
+        log_det = np.log(self.eigenevalues).sum()
+        self.norm_const = -(np.log(2 * np.pi) * len(x) + log_det) / 2
 
     def __len__(self):
         return len(self.x)
 
     def loglike(self, theory):
-        return multivariate_normal_logpdf(theory, self.y, self.cov, self.inv_cov,
-                                          self.log_det)
+        delta = self.y - theory
+        return -0.5 * self._fast_chi_squared(self.inv_cov, delta) + self.norm_const
 
 
 class MultiGaussianData(GaussianData):
@@ -108,22 +106,22 @@ def loglike(self, theory):
     def name(self):
         return self.data.name
 
-    @property
-    def cov(self):
-        return self.data.cov
-
     @property
     def inv_cov(self):
         return self.data.inv_cov
 
     @property
-    def log_det(self):
-        return self.data.log_det
+    def cov(self):
+        return self.data.cov
+
+    @property
+    def norm_const(self):
+        return self.data.norm_const
 
     @property
     def labels(self):
         return [x for y in [[name] * len(d) for
-                name, d in zip(self.names, self.data_list)] for x in y]
+                            name, d in zip(self.names, self.data_list)] for x in y]
 
     def _index_range(self, name):
         if name not in self.names:
@@ -157,6 +155,8 @@ def _assemble_data(self):
         self._data = GaussianData(" + ".join(self.names), x, y, cov)
 
     def plot_cov(self, **kwargs):
+        import holoviews as hv
+
         data = [
             (f"{li}: {self.data.x[i]}", f"{lj}: {self.data.x[j]}", self.cov[i, j])
             for i, li in zip(range(len(self.data)), self.labels)