[pre-commit.ci] pre-commit autoupdate (#49)

* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/pre-commit/pre-commit-hooks: v4.6.0 → v5.0.0](pre-commit/pre-commit-hooks@v4.6.0...v5.0.0) - [github.com/psf/black: 24.4.0 → 24.10.0](psf/black@24.4.0...24.10.0) * Update testing.yml * change likelihood name * add foreground * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add foregrounds * upgrade data files --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Xavier Garrido <xavier.garrido@gmail.com>
xgarrido · Oct 8, 2024 · cbf1213 · cbf1213
1 parent 3e62e93
commit cbf1213
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diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml
@@ -15,6 +15,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
 
     steps:
     - uses: actions/checkout@v4
@@ -27,8 +28,7 @@ jobs:
     - name: Set gfortran link on macos-latest
       if: matrix.os == 'macos-latest'
       run: |
-        set -x
-        sudo ln -s /usr/local/bin/gfortran-11 /usr/local/bin/gfortran
+        ln -s gfortran-12 $(dirname $(which gfortran-12))/gfortran
         gfortran --version
 
     - name: Install dependencies via pip

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,7 +1,7 @@
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.6.0
+  rev: v5.0.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
@@ -16,7 +16,7 @@ repos:
     exclude: docs/tutorials
 
 - repo: https://github.com/psf/black
-  rev: "24.4.0"
+  rev: "24.10.0"
   hooks:
   - id: black
   - id: black-jupyter
diff --git a/cobaya_utilities/fisher.py b/cobaya_utilities/fisher.py
@@ -76,13 +76,17 @@ def compute_fisher_matrix(
         **likelihood_info,
         **{
             "params": _get_sampled_params(params),
-            "theory": {"camb": {"extra_args": {"lens_potential_accuracy": 1}}},
+            "theory": {
+                "camb": {"extra_args": {"lens_potential_accuracy": 1}},
+                "mflike.BandpowerForeground": None,
+            },
         },
     }
 
     model = get_model(info, packages_path=packages_path)
     likelihood = model.likelihood[likelihood_name]
     theory = model.theory["camb"]
+    foregrounds = model.theory["mflike.BandpowerForeground"]
 
     # First grab the constant params and then update with the sampled one. We finally check for
     # missing parameters
@@ -107,7 +111,9 @@ def _get_power_spectra(epsilon):
                 }
             )
             model.logposterior(point)
-            return likelihood._get_power_spectra(theory.get_Cl(ell_factor=True), **point)
+            return likelihood._get_power_spectra(
+                theory.get_Cl(ell_factor=True), foregrounds.get_fg_totals(), **point
+            )
 
         delta = (_get_power_spectra(+epsilon) - _get_power_spectra(-epsilon)) / 2 / epsilon
         if defaults[param] != 0:

diff --git a/cobaya_utilities/tests/data/chains/mcmc.1.txt b/cobaya_utilities/tests/data/chains/mcmc.1.txt
diff --git a/cobaya_utilities/tests/data/chains/mcmc.covmat b/cobaya_utilities/tests/data/chains/mcmc.covmat
@@ -1,3 +1,3 @@
 # a b
-8.989244019214552572e-02 4.245564610023024738e-02
-4.245564610023024738e-02 1.720198303808555818e-01
+8.199574334476460169e-02 3.315608477928922576e-02
+3.315608477928921882e-02 1.566113685937853273e-01
diff --git a/cobaya_utilities/tests/data/chains/mcmc.log b/cobaya_utilities/tests/data/chains/mcmc.log
diff --git a/cobaya_utilities/tests/data/chains/mcmc.progress b/cobaya_utilities/tests/data/chains/mcmc.progress
@@ -1,30 +1,23 @@
 #       N                  timestamp acceptance_rate         Rminus1      Rminus1_cl
- 64.000000 2023-04-04T12:26:16.479221  0.201893  0.468959 NaN
- 128.000000 2023-04-04T12:26:16.638957  0.230631  0.128617 NaN
- 192.000000 2023-04-04T12:26:16.760950  0.26087  0.036854 NaN
- 256.000000 2023-04-04T12:26:16.891122  0.29124  0.069107 NaN
- 320.000000 2023-04-04T12:26:17.004582  0.317776  0.092753 NaN
- 384.000000 2023-04-04T12:26:17.133308  0.318144  0.057716 NaN
- 448.000000 2023-04-04T12:26:17.266454  0.31728  0.088818 NaN
- 512.000000 2023-04-04T12:26:17.407262  0.317421  0.079942 NaN
- 576.000000 2023-04-04T12:26:17.539775  0.326901  0.073043 NaN
- 640.000000 2023-04-04T12:26:17.694220  0.321447  0.060883 NaN
- 704.000000 2023-04-04T12:26:17.822126  0.321314  0.047169 NaN
- 768.000000 2023-04-04T12:26:17.947060  0.320534  0.023891 NaN
- 832.000000 2023-04-04T12:26:18.094098  0.316591  0.03396 NaN
- 896.000000 2023-04-04T12:26:18.223429  0.316832  0.051742 NaN
- 960.000000 2023-04-04T12:26:18.364227  0.314136  0.019223 NaN
- 1024.000000 2023-04-04T12:26:18.508209  0.309646  0.007569 NaN
- 1088.000000 2023-04-04T12:26:18.623970  0.312285  0.016516 NaN
- 1152.000000 2023-04-04T12:26:18.771142  0.310345  0.019428 NaN
- 1216.000000 2023-04-04T12:26:18.915678  0.308865  0.01046 NaN
- 1280.000000 2023-04-04T12:26:19.055133  0.308137  0.015631 NaN
- 1344.000000 2023-04-04T12:26:19.188437  0.307551  0.023413 NaN
- 1408.000000 2023-04-04T12:26:19.340717  0.305423  0.026334 NaN
- 1472.000000 2023-04-04T12:26:19.467083  0.30622  0.015438 NaN
- 1536.000000 2023-04-04T12:26:19.597414  0.305246  0.007206 NaN
- 1600.000000 2023-04-04T12:26:19.735379  0.304298  0.010107 NaN
- 1664.000000 2023-04-04T12:26:19.883041  0.30365  0.007922 NaN
- 1728.000000 2023-04-04T12:26:20.017143  0.302839  0.012971 NaN
- 1792.000000 2023-04-04T12:26:20.166836  0.301176  0.009886 NaN
- 1856.000000 2023-04-04T12:26:20.293572  0.301641  0.005365  0.096527
+ 64.0 2024-10-08T09:48:32.105775  0.201893  0.476479 NaN
+ 128.0 2024-10-08T09:48:32.260897  0.249513  0.212267 NaN
+ 192.0 2024-10-08T09:48:32.435399  0.254305  0.031789 NaN
+ 256.0 2024-10-08T09:48:32.559366  0.294931  0.03945 NaN
+ 320.0 2024-10-08T09:48:32.729418  0.299065  0.031068 NaN
+ 384.0 2024-10-08T09:48:32.905298  0.28764  0.057857 NaN
+ 448.0 2024-10-08T09:48:33.057740  0.287364  0.013654 NaN
+ 512.0 2024-10-08T09:48:33.194020  0.295783  0.022733 NaN
+ 576.0 2024-10-08T09:48:33.346912  0.297828  0.015326 NaN
+ 640.0 2024-10-08T09:48:33.520484  0.294795  0.022256 NaN
+ 704.0 2024-10-08T09:48:33.663743  0.300085  0.018617 NaN
+ 768.0 2024-10-08T09:48:33.832003  0.300352  0.008063 NaN
+ 832.0 2024-10-08T09:48:33.992033  0.299173  0.015152 NaN
+ 896.0 2024-10-08T09:48:34.155622  0.297674  0.045098 NaN
+ 960.0 2024-10-08T09:48:34.293355  0.301129  0.026661 NaN
+ 1024.0 2024-10-08T09:48:34.434713  0.304853  0.023714 NaN
+ 1088.0 2024-10-08T09:48:34.593731  0.303741  0.015738 NaN
+ 1152.0 2024-10-08T09:48:34.776615  0.299143  0.016022 NaN
+ 1216.0 2024-10-08T09:48:34.948338  0.297602  0.0212 NaN
+ 1280.0 2024-10-08T09:48:35.119579  0.296915  0.020767 NaN
+ 1344.0 2024-10-08T09:48:35.295231  0.295385  0.009107 NaN
+ 1408.0 2024-10-08T09:48:35.443081  0.29736  0.005808  0.104905
diff --git a/cobaya_utilities/tests/data/chains/mcmc.updated.yaml b/cobaya_utilities/tests/data/chains/mcmc.updated.yaml
@@ -70,6 +70,6 @@ sampler:
     oversample: null
     drag_limits: null
     fallback_covmat_scale: 4
-    version: '3.3'
+    version: 3.5.4
 output: mcmc
-version: '3.3'
+version: 3.5.4
diff --git a/cobaya_utilities/tests/utilities.py b/cobaya_utilities/tests/utilities.py
@@ -70,7 +70,8 @@ def generate_mcmc():
 
 
 def generate_pickle(data):
-    pickle.dump(data, open(os.path.join(data_path, "reference.pkl"), "wb"))
+    with open(os.path.join(data_path, "reference.pkl"), "wb") as f:
+        pickle.dump(data, f)
 
 
 def get_reference():
@@ -135,7 +136,7 @@ def generate_chains():
 
 all_params = {**cosmo_params, **nuisance_params}
 
-likelihood_name = "mflike.MFLike"
+likelihood_name = "mflike.TTTEEE"
 likelihood_config = {
     "input_file": "LAT_simu_sacc_00000.fits",
     "cov_Bbl_file": "data_sacc_w_covar_and_Bbl.fits",

diff --git a/cobaya_utilities/tools.py b/cobaya_utilities/tools.py
@@ -613,7 +613,7 @@ def get_sampled_parameters(
             if len(found) == 0:
                 continue
             param, loc, scale = found[0]
-            external_priors[param] = f"$\mathcal{{G}}({float(loc)}, {float(scale)})$"
+            external_priors[param] = rf"$\mathcal{{G}}({float(loc)}, {float(scale)})$"
         for param in sampled_params.get(name, []):
             if param not in (params := updated_yaml.get("params")):
                 raise ValueError("Sampled paremeter can not be found within input parameters !")
@@ -627,11 +627,13 @@ def get_sampled_parameters(
                 if dist == "norm":
                     loc, scale = input_priors.get("loc"), input_priors.get("scale")
                     params_info.setdefault((name, "prior"), []).append(
-                        f"$\mathcal{{G}}({loc}, {scale})$"
+                        rf"$\mathcal{{G}}({loc}, {scale})$"
                     )
             elif input_priors.get("min") or input_priors.get("max"):
                 min, max = input_priors.get("min"), input_priors.get("max")
-                params_info.setdefault((name, "prior"), []).append(f"$\mathcal{{U}}({min}, {max})$")
+                params_info.setdefault((name, "prior"), []).append(
+                    rf"$\mathcal{{U}}({min}, {max})$"
+                )
 
     # print(params_info)
     df = pd.DataFrame.from_dict(params_info, orient="index").T.fillna("").drop_duplicates()

diff --git a/pyproject.toml b/pyproject.toml
@@ -21,7 +21,7 @@ classifiers = [
   "Topic :: Education",
 ]
 requires-python = ">=3.9.0"
-dependencies = ["cobaya>=3.4.1", "seaborn", "tqdm"]
+dependencies = ["cobaya>=3.4.1", "seaborn", "tqdm", "jinja2"]
 
 [project.optional-dependencies]
 test = ["pytest", "pytest-cov", "camb", "mflike"]