Add an example using cosmopower

docs/conf.py

@@ -5,42 +5,43 @@
 
 # Import SOLikeT (for autodoc)
 import sys
+
 sys.path.insert(0, "..")
 
 # Create some mock imports
 from unittest import mock
+
 MOCK_MODULES = ["cosmopower", "tensorflow", "pyccl", "camb"]
 for module in MOCK_MODULES:
     sys.modules[module] = mock.Mock()
 
 import soliket
 
-__all__ = ['soliket']
+__all__ = ["soliket"]
 
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'SOLikeT'
-copyright = '2023, The SO Collaboration'
-author = 'The SO Collaboration'
+project = "SOLikeT"
+copyright = "2023, The SO Collaboration"
+author = "The SO Collaboration"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    "sphinx.ext.autodoc", # Generate doc pages from source docstrings
-    "sphinx.ext.viewcode", # Generate links to source code
-    "sphinx.ext.mathjax", # Mathematical symbols
-    "sphinx_rtd_theme", # readthedocs theme
+    "sphinx.ext.autodoc",  # Generate doc pages from source docstrings
+    "sphinx.ext.viewcode",  # Generate links to source code
+    "sphinx.ext.mathjax",  # Mathematical symbols
+    "sphinx_rtd_theme",  # readthedocs theme
 ]
 
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
-
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'sphinx_rtd_theme'
-html_static_path = ['_static']
+html_theme = "sphinx_rtd_theme"
+html_static_path = ["_static"]

examples/example_2.yaml

@@ -0,0 +1,124 @@
+debug: False
+
+# Choose a sampler.
+# Running the evaluate sampler will compute the likelihood
+# only at a single value of parameters
+sampler:
+  evaluate: null
+
+# Specify the output location and file prefix
+output: output/example_2
+
+# Specify which Likelihoods we would like to use
+# Any options for the Likelihood will use their default values unless specified here
+# In this case the options and defaults are specified in soliket/mflike/MFLike.yaml
+# Note that MFLike is a Cobaya `installable likelihood`.
+# When running this yaml file, or calling `cobaya-install example_1.yaml` the required
+# installable components will automatically be downloaded and installed. 
+# Note that for the soliket MFLike likelihood we are required to calculate:
+# - CMB theory power spectra (from CAMB theory below)
+# - Multi-frequency bandpass calibrations (from soliket.BandPass theory below)
+# - Multi-frequency foregrounds (from soliket.Foreground theory below)
+# - The combination of the above components (from soliket.TheoryForge_MFLike theory below)
+likelihood:
+  soliket.MFLike:
+    data_folder: MFLike/v0.8
+    input_file: LAT_simu_sacc_00000.fits
+    cov_Bbl_file: data_sacc_w_covar_and_Bbl.fits
+    lmax_theory: 1500
+    defaults:
+      polarizations: ['TT', 'TE', 'ET', 'EE']
+      scales:
+        TT: [30, 1500]
+        TE: [30, 1500]
+        ET: [30, 1500]
+        EE: [30, 1500]
+      symmetrize: False
+      lcuts: 1500
+
+# Specify the Theory codes which will compute observables to be compared with the data
+# in the Likelihood.
+# Here we specify the CAMB Einstein-Boltzmann code, with a number of choices made on
+# precision and neutrino model.
+theory:
+  soliket.CosmoPower:
+    network_path: soliket/cosmopower/data/CP_paper
+    network_settings:
+      tt:
+        type: NN
+        log: True
+        filename: cmb_TT_NN
+        has_ell_factor: False
+      ee:
+        type: NN
+        log: True
+        filename: cmb_EE_NN
+        has_ell_factor: False
+      te:
+        type: PCAplusNN
+        log: False
+        filename: cmb_TE_PCAplusNN
+        has_ell_factor: False
+
+  soliket.BandPass:
+    stop_at_error: True
+
+  soliket.Foreground:
+    stop_at_error: True
+    spectra:
+        polarizations: ["tt", "te", "ee"]
+        lmin: 2
+        lmax: 1500
+        exp_ch: ["LAT_150"]
+        eff_freqs: [150.]
+
+  soliket.TheoryForge_MFLike:
+    spectra:
+      polarizations: ['tt', 'te', 'ee']
+      lmin: 2
+      lmax: 1500
+    stop_at_error: True
+
+# Specify the parameter values at which to compute the likelihood
+params:
+  H0:
+    latex: H_0
+    derived: 'lambda h: h * 100.'
+  h:
+    latex: h_0
+    value: 0.677
+  logA:
+    # Dropped parameters are sampled but not passed to Likelihood/Theory codes
+    # Here it is As (specified below) which is passed to the Likelihood/Theory code.
+    # drop: true
+    latex: \log(10^{10} A_\mathrm{s})
+    value: 3.05
+  As:
+    value: 'lambda logA: 1e-10*np.exp(logA)'
+  ombh2:
+    latex: \Omega_\mathrm{b} h^2
+    value: 0.0224
+  omch2:
+    latex: \Omega_c h^2
+    value: 0.1202
+  ns:
+    latex: n_s
+    value: 0.9649
+  Alens:
+    latex: A_lens
+    value: 1.0
+  tau:
+    latex: \tau_\mathrm{reio}
+    value: 0.0554
+  mnu1:
+    value: 0.0
+    drop: True
+  mnu2:
+    value: 'lambda: np.sqrt(7.5e-5)'
+    drop: True
+  mnu3:
+    value: 'lambda: np.sqrt(2.5e-3)'
+    drop: True
+  mnu:
+    value: 'lambda mnu1, mnu2 ,mnu3: mnu1 + mnu2 + mnu3'
+    latex: \sum m_\nu

setup.py

@@ -26,7 +26,7 @@
   https://soliket.readthedocs.io/en/latest/developers.html#checking-code-in-development
 """
 
-if 'test' in sys.argv:
+if "test" in sys.argv:
     print(TEST_HELP)
     sys.exit(1)
 
@@ -44,7 +44,7 @@
   https://soliket.readthedocs.io/en/latest/developers.html#documentation
 """
 
-if 'build_docs' in sys.argv or 'build_sphinx' in sys.argv:
+if "build_docs" in sys.argv or "build_sphinx" in sys.argv:
     print(DOCS_HELP)
     sys.exit(1)
 
@@ -59,5 +59,9 @@
     version = '{version}'
 """.lstrip()
 
-setup(use_scm_version={'write_to': os.path.join('soliket', 'version.py'),
-                       'write_to_template': VERSION_TEMPLATE})
+setup(
+    use_scm_version={
+        "write_to": os.path.join("soliket", "version.py"),
+        "write_to_template": VERSION_TEMPLATE,
+    }
+)

soliket/__init__.py

@@ -11,12 +11,16 @@
 from .ccl import CCL
 from .clusters import ClusterLikelihood
 from .cosmopower import CosmoPower, CosmoPowerDerived
-from .cross_correlation import (CrossCorrelationLikelihood,
-                                GalaxyKappaLikelihood, ShearKappaLikelihood)
+from .cross_correlation import (
+    CrossCorrelationLikelihood,
+    GalaxyKappaLikelihood,
+    ShearKappaLikelihood,
+)
 from .foreground import Foreground
 from .gaussian import GaussianLikelihood, MultiGaussianLikelihood
 from .lensing import LensingLikelihood, LensingLiteLikelihood
 from .mflike import MFLike, TheoryForge_MFLike
+
 # from .studentst import StudentstLikelihood
 from .ps import BinnedPSLikelihood, PSLikelihood
 from .xcorr import XcorrLikelihood

soliket/bandpass/bandpass.py

@@ -127,10 +127,11 @@ class BandPass(Theory):
     external_bandpass: dict
 
     def initialize(self):
-
-        self.expected_params_bp = ["bandint_shift_LAT_93",
-                                   "bandint_shift_LAT_145",
-                                   "bandint_shift_LAT_225"]
+        self.expected_params_bp = [
+            "bandint_shift_LAT_93",
+            "bandint_shift_LAT_145",
+            "bandint_shift_LAT_225",
+        ]
 
         self.exp_ch = None
         # self.eff_freqs = None
@@ -148,37 +149,46 @@ def initialize(self):
 
         self.bandint_external_bandpass = bool(self.external_bandpass)
         if self.bandint_external_bandpass:
-            path = os.path.normpath(os.path.join(self.data_folder,
-                                                 self.external_bandpass["path"]))
+            path = os.path.normpath(
+                os.path.join(self.data_folder, self.external_bandpass["path"])
+            )
             arrays = os.listdir(path)
             self._init_external_bandpass_construction(path, arrays)
 
-        if (not self.read_from_sacc and not self.use_top_hat_band
-                and not self.bandint_external_bandpass):
+        if (
+            not self.read_from_sacc
+            and not self.use_top_hat_band
+            and not self.bandint_external_bandpass
+        ):
             raise LoggedError(
-                self.log, "fill the dictionaries in the yaml file for"
-                          "either reading the passband from sacc file (mflike default)"
-                          "or an external passband or building a top-hat one!"
+                self.log,
+                "fill the dictionaries in the yaml file for"
+                "either reading the passband from sacc file (mflike default)"
+                "or an external passband or building a top-hat one!",
             )
 
     def initialize_with_params(self):
         # Check that the parameters are the right ones
         differences = are_different_params_lists(
-            self.input_params, self.expected_params_bp,
-            name_A="given", name_B="expected")
+            self.input_params,
+            self.expected_params_bp,
+            name_A="given",
+            name_B="expected",
+        )
         if differences:
             raise LoggedError(
-                self.log, "Configuration error in parameters: %r.",
-                differences)
+                self.log, "Configuration error in parameters: %r.", differences
+            )
 
     def must_provide(self, **requirements):
         # bandint_freqs is required by Foreground
         # and requires some params to be computed
         # Assign those from Foreground
         if "bandint_freqs" in requirements:
             self.bands = requirements["bandint_freqs"]["bands"]
-            self.exp_ch = [k.replace("_s0", "") for k in self.bands.keys()
-                           if "_s0" in k]
+            self.exp_ch = [
+                k.replace("_s0", "") for k in self.bands.keys() if "_s0" in k
+            ]
 
     def calculate(self, state, want_derived=False, **params_values_dict):
         r"""
@@ -231,7 +241,7 @@ def _bandpass_construction(self, **params):
         data_are_monofreq = False
         bandint_freqs = []
         for ifr, fr in enumerate(self.exp_ch):
-            bandpar = 'bandint_shift_' + str(fr)
+            bandpar = "bandint_shift_" + str(fr)
             bands = self.bands[f"{fr}_s0"]
             nu_ghz, bp = np.asarray(bands["nu"]), np.asarray(bands["bandpass"])
             if self.use_top_hat_band:
@@ -240,19 +250,25 @@ def _bandpass_construction(self, **params):
                     self.bandint_width = np.full_like(
                         self.exp_ch, self.bandint_width, dtype=float
                     )
-                if self.bandint_nsteps > 1 and np.any(np.array(self.bandint_width) == 0):
+                if self.bandint_nsteps > 1 and np.any(
+                    np.array(self.bandint_width) == 0
+                ):
                     raise LoggedError(
-                        self.log, "One band has width = 0, \
-                                    set a positive width and run again"
+                        self.log,
+                        "One band has width = 0, \
+                                    set a positive width and run again",
                     )
                 # Compute central frequency given bandpass
                 fr = nu_ghz @ bp / bp.sum()
                 if self.bandint_nsteps > 1:
-                    bandlow = fr * (1 - self.bandint_width[ifr] * .5)
-                    bandhigh = fr * (1 + self.bandint_width[ifr] * .5)
-                    nub = np.linspace(bandlow + params[bandpar],
-                                      bandhigh + params[bandpar],
-                                      self.bandint_nsteps, dtype=float)
+                    bandlow = fr * (1 - self.bandint_width[ifr] * 0.5)
+                    bandhigh = fr * (1 + self.bandint_width[ifr] * 0.5)
+                    nub = np.linspace(
+                        bandlow + params[bandpar],
+                        bandhigh + params[bandpar],
+                        self.bandint_nsteps,
+                        dtype=float,
+                    )
                     tranb = _cmb2bb(nub)
                     tranb_norm = np.trapz(_cmb2bb(nub), nub)
                     bandint_freqs.append([nub, tranb / tranb_norm])
@@ -313,7 +329,9 @@ def _external_bandpass_construction(self, **params):
             if not hasattr(bp, "__len__"):
                 bandint_freqs.append(nub)
                 bandint_freqs = np.asarray(bandint_freqs)
-                self.log.info("bandpass is delta function, no band integration performed")
+                self.log.info(
+                    "bandpass is delta function, no band integration performed"
+                )
             else:
                 trans_norm = np.trapz(bp * _cmb2bb(nub), nub)
                 trans = bp / trans_norm * _cmb2bb(nub)