Merge branch 'main' into issue/401/use_qp

.github/workflows/build_check.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - uses: conda-incubator/setup-miniconda@v2
     - name: Install prereq using conda
       run: |
@@ -33,10 +33,10 @@ jobs:
         pip install .
     - name: Install CCL from source
       run: |
-        conda install -c conda-forge cmake swig
+        conda install -c conda-forge cmake swig --override-channels
         git clone https://github.com/LSSTDESC/CCL
         cd CCL
-        git checkout 1a351df
+        git checkout v3.0.0
         pip install .
     - name: Analysing the code with pylint
       run: |

CONTRIBUTING.md

@@ -49,6 +49,9 @@ NOTE: Code is not complete without unit tests and documentation. Please ensure t
 
 To test this, first install the code by running `python setup.py install --user` (required after any change whatsoever to the `.py` files in `clmm/` directory). To run all of the unit tests, run `pytest` in the root package directory. To test the docs, in the root package directory after installing, run `./update_docs`. This script both deletes the old compiled documentation files and rebuilds them. You can view the compiled docs by running `open docs/_build/html/index.html`.
 
+NOTE2: If the changes you are making affect which CCL versions are compatible with the code,
+please update `clmm/theory/_ccl_supported_versions.py`, `README.md` and `INSTALL.md` accordingly.
+
 ## Adding documentation <a name="adding_documentation"></a>
 
 If you are adding documentation either in the form of example jupyter notebooks or new python modules, your documentation will need to compile for our online documentation hosted by the LSST-DESC website: http://lsstdesc.org/CLMM/

INSTALL.md

@@ -17,7 +17,10 @@ To create a specific conda environment for CLMM, we recommend you to check the b
 [Access to the proper environment on cori.nersc.gov](#access_to_the_proper_environment_on_cori).
 
 ### Theory backend installation
-First, choose and install a theory backend for CLMM. This can be CCL (v2.1.0 or later) or NumCosmo (v0.15 or later), or cluster_toolkit and they are installable as follows.
+First, choose and install a theory backend for CLMM.
+This can be CCL (versions between 2.7.1.dev10+gf81b59a4 and 3),
+NumCosmo (v0.15 or later),
+or cluster_toolkit and they are installable as follows.
 
 To install CCL as the theory/cosmology backend, run
 

README.md

@@ -20,7 +20,7 @@ link to this repository: https://github.com/LSSTDESC/CLMM. Please follow the gui
 
 ## Requirements <a name="requirements"></a>
 
-CLMM requires Python version 3.6 or later.  CLMM has the following dependencies:
+CLMM requires Python version 3.8 or later.  CLMM has the following dependencies:
 
 - [NumPy](https://www.numpy.org/) (v1.17 or later)
 - [SciPy](https://scipy.org/) (v1.3 or later)
@@ -35,9 +35,10 @@ CLMM requires Python version 3.6 or later.  CLMM has the following dependencies:
 For the theoretical predictions of the signal, CLMM relies on existing libraries and **at least one of the following must be installed as well**:
 
 - [cluster-toolkit](https://cluster-toolkit.readthedocs.io/en/latest/)
-- [CCL](https://ccl.readthedocs.io/en/latest/) (v2.6 or later)
+- [CCL](https://ccl.readthedocs.io/en/latest/) (versions between 2.7.1.dev10+gf81b59a4 and 3)
 - [NumCosmo](https://numcosmo.github.io/) (v0.15 or later)
 
+
 (See the [INSTALL documentation](INSTALL.md) for more detailed installation instructions.)
 
 For developers, you will also need to install:

clmm/__init__.py

@@ -26,4 +26,4 @@
 )
 from . import support
 
-__version__ = '1.8.0'
+__version__ = "1.10.0"

clmm/cosmology/ccl.py

@@ -113,7 +113,7 @@ def _eval_sigma_crit_core(self, z_len, z_src):
         a_len = self.get_a_from_z(z_len)
         a_src = self.get_a_from_z(z_src)
 
-        return self.be_cosmo.sigma_critical(a_len, a_src) * self.cor_factor
+        return self.be_cosmo.sigma_critical(a_lens=a_len, a_source=a_src) * self.cor_factor
 
     def _eval_linear_matter_powerspectrum(self, k_vals, redshift):
         return ccl.linear_matter_power(self.be_cosmo, k_vals, self.get_a_from_z(redshift))