win test working

pyproject.toml

@@ -103,3 +103,8 @@ exclude_lines = [
     # Don't complain about IPython completion helper
     "def _ipython_key_completions_",
 ]
+
+[tool.pytest.ini_options]
+markers = [
+    "require_ccl",
+]

requirements.txt

@@ -13,7 +13,7 @@ fuzzywuzzy
 astropy
 getdist
 cobaya
-pyccl
+pyccl >= 3.0; platform_system!='Windows'
 sacc
 fgspectra>=1.1.0
 syslibrary

soliket/clusters/clusters.py

@@ -21,14 +21,14 @@
 
 import numpy as np
 import pandas as pd
-import pyccl as ccl
 from scipy.interpolate import interp1d
 
 from soliket.clusters import massfunc as mf
 from soliket.poisson import PoissonLikelihood
 
 from .survey import SurveyData
 from .sz_utils import szutils
+from cobaya import LoggedError
 
 C_KM_S = 2.99792e5
 
@@ -54,8 +54,14 @@ def initialize(self):
 
         self.zarr = np.arange(0, 2, 0.05)
         self.k = np.logspace(-4, np.log10(5), 200)
-        # self.mdef = ccl.halos.MassDef(500, 'critical')
-
+        # self.mdef = self.ccl.halos.MassDef(500, 'critical')
+
+        try:
+            import pyccl as ccl
+        except ImportError:
+            raise LoggedError(self.log, "Could not import ccl. Install pyccl to use ClusterLikelihood.")
+        else:
+            self.ccl = ccl
         super().initialize()
 
     def get_requirements(self):
@@ -82,11 +88,11 @@ def get_requirements(self):
 
     def _get_sz_model(self, cosmo):
         model = SZModel()
-        model.hmf = ccl.halos.MassFuncTinker08(cosmo, mass_def=self.mdef)
-        model.hmb = ccl.halos.HaloBiasTinker10(
+        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 = ccl.halos.HMCalculator(cosmo, model.hmf, model.hmb, self.mdef)
+        model.hmc = self.ccl.halos.HMCalculator(cosmo, model.hmf, model.hmb, self.mdef)
         # model.szk = SZTracer(cosmo)
         return model
 

soliket/lensing/lensing.py

@@ -109,7 +109,7 @@ def initialize(self):
         Cls = self._get_fiducial_Cls()
 
         # Set the fiducial spectra
-        self.ls = np.arange(0, self.lmax)
+        self.ls = np.arange(0, self.lmax, dtype=np.longlong)
         self.fcltt = Cls["tt"][0: self.lmax]
         self.fclpp = Cls["pp"][0: self.lmax]
         self.fclee = Cls["ee"][0: self.lmax]

soliket/tests/conftest.py

@@ -1,4 +1,13 @@
 import pytest
+import sys
+
+
+def pytest_collection_modifyitems(config, items):
+    if sys.platform.startswith('win'):
+        skip_on_windows = pytest.mark.skip(reason="Skipped on Windows")
+        for item in items:
+            if "require_ccl" in item.keywords:
+                item.add_marker(skip_on_windows)
 
 
 @pytest.fixture

soliket/tests/test_ccl.py

@@ -2,17 +2,21 @@
 Check that CCL works correctly.
 """
 import numpy as np
+import pytest
 from cobaya.likelihood import Likelihood
 from cobaya.model import get_model
 
+pytestmark = pytest.mark.require_ccl
+
 
 class CheckLike(Likelihood):
     """
     This is a mock likelihood that simply forces soliket.CCL to calculate
     a CCL object.
     """
+
     def logp(self, **params_values):
-        ccl = self.provider.get_CCL() # noqa F841
+        ccl = self.provider.get_CCL()  # noqa F841
         return -1.0
 
     def get_requirements(self):

soliket/tests/test_clusters.py

@@ -1,6 +1,9 @@
 import numpy as np
+import pytest
 from cobaya.model import get_model
 
+pytestmark = pytest.mark.require_ccl
+
 clusters_like_and_theory = {
     "likelihood": {"soliket.ClusterLikelihood": {"stop_at_error": True}},
     "theory": {
@@ -20,15 +23,13 @@
 
 
 def test_clusters_model(evaluate_one_info, test_cosmology_params):
-
     evaluate_one_info["params"] = test_cosmology_params
     evaluate_one_info.update(clusters_like_and_theory)
 
-    model_fiducial = get_model(evaluate_one_info) # noqa F841
+    model_fiducial = get_model(evaluate_one_info)  # noqa F841
 
 
 def test_clusters_loglike(evaluate_one_info, test_cosmology_params):
-
     evaluate_one_info["params"] = test_cosmology_params
     evaluate_one_info.update(clusters_like_and_theory)
 
@@ -40,7 +41,6 @@ def test_clusters_loglike(evaluate_one_info, test_cosmology_params):
 
 
 def test_clusters_n_expected(evaluate_one_info, test_cosmology_params):
-
     evaluate_one_info["params"] = test_cosmology_params
     evaluate_one_info.update(clusters_like_and_theory)