Remove some over-redundant hints

soliket/bandpass/bandpass.py

@@ -173,7 +173,7 @@ def initialize_with_params(self):
                 self.log, "Configuration error in parameters: %r.",
                 differences)
 
-    def must_provide(self, **requirements: dict):
+    def must_provide(self, **requirements):
         # bandint_freqs is required by Foreground
         # and requires some params to be computed
         # Assign those from Foreground
@@ -209,7 +209,7 @@ def get_bandint_freqs(self) -> dict:
     # Takes care of the bandpass construction. It returns a list of nu-transmittance for
     # each frequency or an array with the effective freqs.
     def _bandpass_construction(
-        self, **params: dict
+        self, **params
     ) -> Union[np.ndarray, List[np.ndarray]]:
         r"""
         Builds the bandpass transmission
@@ -294,7 +294,9 @@ def _init_external_bandpass_construction(self, path: str, exp_ch: List[str]):
             nu_ghz, bp = np.loadtxt(path + "/" + expc, usecols=(0, 1), unpack=True)
             self.external_bandpass.append([expc, nu_ghz, bp])
 
-    def _external_bandpass_construction(self, **params: dict) -> List[np.ndarray]:
+    def _external_bandpass_construction(
+        self, **params
+    ) -> Union[np.ndarray, List[np.ndarray]]:
         r"""
         Builds bandpass transmission
         :math:`\frac{\frac{\partial B_{\nu+\Delta \nu}}{\partial T}

soliket/bias/bias.py

@@ -26,7 +26,7 @@
 function (have a look at the linear bias model for ideas).
 """
 
-from typing import Dict, List, Optional, Set, Tuple, Union
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
 
 import numpy as np
 from cobaya.theory import Theory
@@ -47,13 +47,13 @@ class Bias(Theory):
     _default_z_sampling = 10 ** _logz
     _default_z_sampling[0] = 0
 
-    def initialize(self) -> None:
+    def initialize(self):
         self._var_pairs: Set[Tuple[str, str]] = set()
 
-    def get_requirements(self) -> Dict[str, dict]:
+    def get_requirements(self) -> Dict[str, Any]:
         return {}
 
-    def must_provide(self, **requirements: dict) -> Dict[str, dict]:
+    def must_provide(self, **requirements) -> Dict[str, Any]:
         options = requirements.get("linear_bias") or {}
 
         self.kmax = max(self.kmax, options.get("kmax", self.kmax))
@@ -62,7 +62,7 @@ def must_provide(self, **requirements: dict) -> Dict[str, dict]:
              np.atleast_1d(self.z))))
 
         # Dictionary of the things needed from CAMB/CLASS
-        needs: Dict[str, dict] = {}
+        needs = {}
 
         self.nonlinear = self.nonlinear or options.get("nonlinear", False)
         self._var_pairs.update(
@@ -103,7 +103,7 @@ class Linear_bias(Bias):
     params: dict
 
     def calculate(self, state: dict, want_derived: bool = True,
-                  **params_values_dict) -> None:
+                  **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

@@ -13,7 +13,7 @@ class CashCLikelihood(Likelihood):
 
     enforce_types: bool = True
 
-    def initialize(self) -> None:
+    def initialize(self):
         x, N = self._get_data()
         self.data = CashCData(self.name, N)
 
@@ -23,12 +23,12 @@ def _get_data(self) -> Tuple[np.ndarray, np.ndarray]:
         x = data[:, :-1]
         return x, N
 
-    def _get_theory(self, **kwargs: dict) -> np.ndarray:
+    def _get_theory(self, **kwargs) -> np.ndarray:
         if "cash_test_logp" in kwargs:
             return np.arange(kwargs["cash_test_logp"])
         else:
             raise NotImplementedError
 
-    def logp(self, **params_values: dict) -> float:
+    def logp(self, **params_values) -> float:
         theory = self._get_theory(**params_values)
         return self.data.loglike(theory)

soliket/cash/cash_data.py

@@ -31,7 +31,7 @@ class CashCData:
 
     def __init__(
             self, name: str, N: Union[np.ndarray, float], usestirling: bool = True
-    ) -> None:
+    ):
         self.name = str(name)
         self.data = N
         self.usestirling = usestirling

soliket/ccl/ccl.py

@@ -98,7 +98,7 @@ class CCL(Theory):
     _default_z_sampling[0] = 0
     provider: Provider
 
-    def initialize(self) -> None:
+    def initialize(self):
         try:
             import pyccl as ccl
         except ImportError:
@@ -161,7 +161,7 @@ def get_can_support_params(self) -> Sequence[str]:
         return []
 
     def calculate(self, state: dict, want_derived: bool = True,
-                  **params_values_dict) -> None:
+                  **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/cosmopower/cosmopower.py

@@ -91,7 +91,7 @@
 information on how SOLikeT infers these values.
 """
 import os
-from typing import Dict, Iterable, List, Tuple
+from typing import Any, Dict, List
 
 import numpy as np
 from cobaya.log import LoggedError
@@ -112,7 +112,7 @@ class CosmoPower(BoltzmannBase):
 
     enforce_types: bool = True
 
-    def initialize(self) -> None:
+    def initialize(self):
 
         super().initialize()
 
@@ -283,10 +283,10 @@ def cmb_unit_factor(self, spectra: str,
 
         return res
 
-    def get_can_support_parameters(self) -> Iterable[str]:
+    def get_can_support_parameters(self) -> List[str]:
         return self.all_parameters
 
-    def get_requirements(self) -> Iterable[Tuple[str, str]]:
+    def get_requirements(self) -> Dict[str, Any]:
         requirements = []
         for k in self.all_parameters:
             if k in self.renames.values():
@@ -303,7 +303,7 @@ def get_requirements(self) -> Iterable[Tuple[str, str]]:
 class CosmoPowerDerived(Theory):
     """A theory class that can calculate derived parameters from CosmoPower networks."""
 
-    def initialize(self) -> None:
+    def initialize(self):
         super().initialize()
 
         if self.network_settings is None:
@@ -365,10 +365,10 @@ def calculate(self, state: dict, want_derived: bool = True, **params) -> bool:
     def get_param(self, p) -> float:
         return self.current_state["derived"][self.translate_param(p)]
 
-    def get_can_support_parameters(self) -> Iterable[str]:
+    def get_can_support_parameters(self) -> List[str]:
         return self.input_parameters
 
-    def get_requirements(self) -> Iterable[Tuple[str, str]]:
+    def get_requirements(self) -> Dict[str, Any]:
         requirements = []
         for k in self.input_parameters:
             if k in self.renames.values():
@@ -381,6 +381,6 @@ def get_requirements(self) -> Iterable[Tuple[str, str]]:
 
         return requirements
 
-    def get_can_provide(self) -> Iterable[str]:
+    def get_can_provide(self) -> List[str]:
         return set([par for par in self.derived_parameters
                     if (len(par) > 0 and not par == "_")])

soliket/cross_correlation/cross_correlation.py

@@ -36,14 +36,14 @@ def initialize(self):
         self._get_sacc_data()
         self._check_tracers()
 
-    def get_requirements(self) -> Dict[str, dict]:
+    def get_requirements(self) -> Dict[str, Any]:
         return {"CCL": {"kmax": 10, "nonlinear": True}, "zstar": None}
 
     def _get_CCL_results(self) -> Tuple[CCL, dict]:
         cosmo_dict = self.provider.get_CCL()
         return cosmo_dict["ccl"], cosmo_dict["cosmo"]
 
-    def _check_tracers(self) -> None:
+    def _check_tracers(self):
 
         # check correct tracers
         for tracer_comb in self.sacc_data.get_tracer_combinations():
@@ -72,7 +72,7 @@ def _get_nz(
         z: np.ndarray,
         tracer: Any,
         tracer_name: str,
-        **params_values: dict
+        **params_values
     ) -> np.ndarray:
         if self.z_nuisance_mode == 'deltaz':
             bias = params_values[f'{tracer_name}_deltaz']
@@ -82,7 +82,7 @@ def _get_nz(
 
         return nz_biased
 
-    def _get_sacc_data(self, **params_values: dict) -> None:
+    def _get_sacc_data(self, **params_values):
         self.sacc_data = sacc.Sacc.load_fits(self.datapath)
 
         if self.use_spectra == 'all':
@@ -109,7 +109,7 @@ def _construct_ell_bins(self) -> np.ndarray:
         return np.concatenate(ell_eff)
 
     def _get_data(
-        self, **params_values: dict
+        self, **params_values
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         data_auto = np.loadtxt(self.auto_file)
         data_cross = np.loadtxt(self.cross_file)
@@ -138,7 +138,7 @@ def get_binning(self, tracer_comb: tuple) -> Tuple[np.ndarray, np.ndarray]:
 
         return ells_theory, w_bins
 
-    def logp(self, **params_values: dict) -> float:
+    def logp(self, **params_values) -> float:
         theory = self._get_theory(**params_values)
         return self.data.loglike(theory)
 
@@ -150,7 +150,7 @@ class GalaxyKappaLikelihood(CrossCorrelationLikelihood):
     _allowable_tracers: ClassVar[List[str]] = ['cmb_convergence', 'galaxy_density']
     params: dict
 
-    def _get_theory(self, **params_values: dict) -> np.ndarray:
+    def _get_theory(self, **params_values) -> np.ndarray:
         ccl, cosmo = self._get_CCL_results()
 
         tracer_comb = self.sacc_data.get_tracer_combinations()
@@ -194,7 +194,7 @@ class ShearKappaLikelihood(CrossCorrelationLikelihood):
     ia_mode: Optional[str]
     params: dict
 
-    def _get_theory(self, **params_values: dict) -> np.ndarray:
+    def _get_theory(self, **params_values) -> np.ndarray:
         ccl, cosmo = self._get_CCL_results()
         cl_binned_list: List[np.ndarray] = []
 

soliket/foreground/foreground.py

@@ -306,7 +306,7 @@ def _get_foreground_model(
                             fg_dict[s, "all", f1, f2] += fg_dict[s, comp, f1, f2]
         return fg_dict
 
-    def must_provide(self, **requirements: dict) -> dict:
+    def must_provide(self, **requirements) -> dict:
         # fg_dict is required by theoryforge
         # and requires some params to be computed
         # Assign those from theoryforge
@@ -323,7 +323,7 @@ def must_provide(self, **requirements: dict) -> dict:
             return {"bandint_freqs": {"bands": self.bands}}
         return {}
 
-    def get_bandpasses(self, **params: dict) -> np.ndarray:
+    def get_bandpasses(self, **params) -> np.ndarray:
         """
         Gets bandpass transmissions from the ``BandPass`` class.
         """

soliket/gaussian/gaussian.py

@@ -21,7 +21,7 @@ class GaussianLikelihood(Likelihood):
 
     enforce_types: bool = True
 
-    def initialize(self) -> None:
+    def initialize(self):
         x, y = self._get_data()
         cov = self._get_cov()
         self.data = GaussianData(self.name, x, y, cov, self.ncovsims)
@@ -34,16 +34,16 @@ def _get_cov(self) -> np.ndarray:
         cov = np.loadtxt(self.covpath)
         return cov
 
-    def _get_theory(self, **kwargs: dict) -> np.ndarray:
+    def _get_theory(self, **kwargs) -> np.ndarray:
         raise NotImplementedError
 
-    def logp(self, **params_values: dict) -> float:
+    def logp(self, **params_values) -> float:
         theory = self._get_theory(**params_values)
         return self.data.loglike(theory)
 
 
 class CrossCov(dict):
-    def save(self, path: str) -> None:
+    def save(self, path: str):
         np.savez(path, **{str(k): v for k, v in self.items()})
 
     @classmethod
@@ -58,7 +58,7 @@ class MultiGaussianLikelihood(GaussianLikelihood):
     options: Optional[Sequence] = None
     cross_cov_path: Optional[str] = None
 
-    def __init__(self, info: dict = empty_dict, **kwargs) -> None:
+    def __init__(self, info: dict = empty_dict, **kwargs):
 
         if 'components' in info:
             self.likelihoods: List[Likelihood] = [
@@ -72,15 +72,15 @@ def __init__(self, info: dict = empty_dict, **kwargs) -> None:
 
         super().__init__(info=default_info, **kwargs)
 
-    def initialize(self) -> None:
+    def initialize(self):
         self.cross_cov: Optional[CrossCov] = CrossCov.load(self.cross_cov_path)
 
         data_list = [like.data for like in self.likelihoods]
         self.data = MultiGaussianData(data_list, self.cross_cov)
 
         self.log.info('Initialized.')
 
-    def initialize_with_provider(self, provider: Provider) -> None:  # pragma: no cover
+    def initialize_with_provider(self, provider: Provider):  # pragma: no cover
         for like in self.likelihoods:
             like.initialize_with_provider(provider)
         # super().initialize_with_provider(provider)

soliket/gaussian/gaussian_data.py

@@ -17,7 +17,7 @@ class GaussianData:
     _fast_chi_squared = staticmethod(functions.chi_squared)
 
     def __init__(self, name: str, x: Sequence[float], y: Sequence[float], cov: np.ndarray,
-                 ncovsims: Optional[int] = None) -> None:
+                 ncovsims: Optional[int] = None):
 
         self.name: str = str(name)
         self.ncovsims: Optional[int] = ncovsims
@@ -64,7 +64,7 @@ def __init__(
         self,
         data_list: List[GaussianData],
         cross_covs: Optional[Dict[Tuple[str, str], np.ndarray]] = None,
-    ) -> None:
+    ):
 
         if cross_covs is None:
             cross_covs = {}
@@ -148,7 +148,7 @@ def _slice(self, *names: str) -> slice:
 
         return np.s_[tuple(slice(*self._index_range(n)) for n in names)]
 
-    def _assemble_data(self) -> None:
+    def _assemble_data(self):
         x = np.concatenate([d.x for d in self.data_list])
         y = np.concatenate([d.y for d in self.data_list])
 
@@ -161,7 +161,7 @@ def _assemble_data(self) -> None:
 
         self._data = GaussianData(" + ".join(self.names), x, y, cov)
 
-    def plot_cov(self, **kwargs) -> None:
+    def plot_cov(self, **kwargs):
         import holoviews as hv
 
         data = [