Quick fix to skip mass-richness relation (#79)

* add quick fix to skip mass-richness relation

* fix typo in docstring

* fix linebreaks

* fix bad call

* fix typos

* fix linebreaks

* fix lims in cl_cov without mproxy

* update tests

* format code

* add tests

* rm old test

* Requiring pyccl < 2.8.0 due to large amount of breaking changes.

* get has_mproxy from config

* simplify mass_proxy condition

pyproject.toml

@@ -29,7 +29,7 @@ dependencies = [
     "pyyaml",
     "pytest",
     "pytest-rerunfailures",
-    "pyccl>=2.5.0",
+    "pyccl>=2.5.0,<2.8.0",
     "sacc>=0.8",
     "camb",
     "healpy",

tests/test_covariance_clusters.py

@@ -159,6 +159,21 @@ def test_integral_mass(
     assert test == pytest.approx(reference_val, rel=1e-4)
 
 
+@pytest.mark.parametrize(
+    "z, reference_val",
+    [
+        (0.5, 3.8e-05),  # a proper value must be added here
+    ],
+)
+def test_integral_mass_no_mproxy(
+    mock_covariance_gauss: CovarianceClusters, z, reference_val
+):
+    mock_covariance_gauss.richness_bins = np.linspace(13.5, 14, 4)
+    mock_covariance_gauss.has_mproxy = False
+    test = mock_covariance_gauss.mass_richness_integral(z, 0)
+    assert test == pytest.approx(reference_val, rel=1e-1)
+
+
 def test_mass_richness(mock_covariance_gauss: CovarianceClusters):
     reference_min = 0.0009528852621284171
 

tjpcov/covariance_clusters.py

@@ -47,6 +47,9 @@ def __init__(self, config, survey_area=4 * np.pi):
             cosmo, self.z_lower_limit, self.z_upper_limit
         )
 
+        # Quick key to skip P(Richness|M)
+        self.has_mproxy = self.config.get("has_mproxy", True)
+
     def load_from_cosmology(self, cosmo):
         """Values used by the covariance calculation that come from a CCL
         cosmology object.  Derived attributes from the cosmology are set here.
@@ -302,21 +305,27 @@ def integrand(ln_m):
                 )
                 argument *= halo_bias
 
-            mass_richness = self.mass_richness(ln_m, richness_i)
-            argument *= mass_richness
+            if self.has_mproxy:
+                argument *= self.mass_richness(ln_m, richness_i)
 
             return argument
 
-        return self._quad_integrate(integrand, self.min_mass, self.max_mass)
+        if self.has_mproxy:
+            m_integ_lower, m_integ_upper = self.min_mass, self.max_mass
+        else:
+            m_integ_lower = np.log(10) * self.richness_bins[richness_i]
+            m_integ_upper = np.log(10) * self.richness_bins[richness_i + 1]
+
+        return self._quad_integrate(integrand, m_integ_lower, m_integ_upper)
 
     def partial_SSC(self, z, bin_z_j, bin_lbd_j, approx=True):
         """Calculate part of the super sample covariance, or the non-diagonal
         correlation between two point functions whose observed modes are larger
         than the survey size.
 
         Args:
-            z1 (float): redshift
-            bin_z_j (int): redshift bin i
+            z (float): redshift
+            bin_z_j (int): redshift bin j
             bin_lbd_j (int): richness bin j
             approx (bool, optional): Will only calculate the mass richness
             integral once and multiply at end. Defaults to True.