Merge branch 'main' into issue/588/miscentering_therory

.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: |

INSTALL.md

@@ -13,13 +13,13 @@
 ## Basic procedure <a name="basic_install"></a>
 
 Here we provide a quick guide for a basic instalation, this will install all the packages in your current environment.
-To create a specific conda environment for CLMM, we recommend you to check the begining of section 
+To create a specific conda environment for CLMM, we recommend you to check the begining of section
 [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 (versions between 2.6.0 and 2.7.1.dev9+g1a351df6),
-NumCosmo (v0.15 or later),
+This can be CCL (versions between 2.7.1.dev10+gf81b59a4 and 3),
+NumCosmo (v0.19 or later),
 or cluster_toolkit and they are installable as follows.
 
 To install CCL as the theory/cosmology backend, run
@@ -60,7 +60,7 @@ Now, you can install CLMM and its dependencies as
     pip install pytest sphinx sphinx_rtd_theme
     pip install jupyter  # need to have jupyter notebook tied to this environment, you can then see the environment in jupyter.nersc.gov
     git clone https://github.com/LSSTDESC/CLMM.git  # If you'd like to contribute but don't have edit permissions to the CLMM repo, see below how to fork the repo instead.
-    cd CLMM   
+    cd CLMM
     python setup.py install     # build from source
 ```
 
@@ -72,9 +72,9 @@ If you have access to NERSC, this will likely be the easiest to make sure you ha
     module load python  # Also loads anaconda
     conda create --name clmmenv  # Create an anaconda environment for clmm
     source activate clmmenv  # switch to your newly created environment
-    conda install pip  # need pip to install everything else necessary for clmm 
+    conda install pip  # need pip to install everything else necessary for clmm
     conda install ipython # need to have the ipython tied to this environment
-    conda install -c conda-forge firefox  # Need a browser to view jupyter notebooks  
+    conda install -c conda-forge firefox  # Need a browser to view jupyter notebooks
 ```
 
 Note, for regular contributions and use, we recommend adding `module load python` to your `~/.bashrc` so you have anaconda installed every time you log in.  You will subseqeuntly also want to be in the correct environment whenever working with `clmm`, which means running `source activate clmmenv` at the start of each session.

README.md

@@ -24,7 +24,7 @@ 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)
-- [Astropy](https://www.astropy.org/) (v4.0 or later for units and cosmology dependence)  
+- [Astropy](https://www.astropy.org/) (v4.0 or later for units and cosmology dependence)
 (Please avoid Astropy v5.0 since there is bug breaking CCL backend. It has been fixed in Astropy v5.0.1.)
 - [Matplotlib](https://matplotlib.org/) (for plotting and going through tutorials)
 
@@ -35,8 +35,8 @@ CLMM requires Python version 3.8 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/) (versions between 2.6.0 and 2.7.1.dev9+g1a351df6)
-- [NumCosmo](https://numcosmo.github.io/) (v0.15 or later)
+- [CCL](https://ccl.readthedocs.io/en/latest/) (versions between 2.7.1.dev10+gf81b59a4 and 3)
+- [NumCosmo](https://numcosmo.github.io/) (v0.19 or later)
 
 
 (See the [INSTALL documentation](INSTALL.md) for more detailed installation instructions.)
@@ -91,8 +91,8 @@ non-DESC project concept and progress to be presented to the working group,
 so working group members can help co-identify tools and/or ongoing development
 that might mutually benefit your non-DESC project and ongoing DESC projects.
 
-**External Projects by Non-DESC members**: If you are not from the DESC 
-community, you are also welcome to contact CLMM Topical Team leads to introduce 
+**External Projects by Non-DESC members**: If you are not from the DESC
+community, you are also welcome to contact CLMM Topical Team leads to introduce
 your project and share feedback.
 
 
@@ -123,7 +123,7 @@ issue](https://github.com/LSSTDESC/CLMM/issues).
 
 The current leads of the LSST DESC CLMM Topical Team are Michel Aguena
 (m-aguena, [email protected]) and Marina Ricci (mricci,
-marina.ricci@lmu.de)
+marina.ricci@apc.in2p3.fr)
 
 
 # Acknowledgements <a name="acknowledgements"></a>

clmm/__init__.py

@@ -26,4 +26,4 @@
 )
 from . import support
 
-__version__ = "1.8.2"
+__version__ = "1.11.0"

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))

clmm/cosmology/numcosmo.py

@@ -33,6 +33,7 @@ class NumCosmoCosmology(CLMMCosmology):
 
     def __init__(self, dist=None, dist_zmax=15.0, **kwargs):
         self.dist = None
+        self.smd = None
 
         super().__init__(**kwargs)
 
@@ -54,6 +55,7 @@ def __init__(self, dist=None, dist_zmax=15.0, **kwargs):
         self.smd = Nc.WLSurfaceMassDensity.new(self.dist)
         self.smd.prepare_if_needed(self.be_cosmo)
 
+
     def _init_from_cosmo(self, be_cosmo):
         assert isinstance(be_cosmo, Nc.HICosmo)
         assert isinstance(be_cosmo, Nc.HICosmoDECpl)
@@ -63,7 +65,7 @@ def _init_from_cosmo(self, be_cosmo):
 
     def _init_from_params(self, H0, Omega_b0, Omega_dm0, Omega_k0):
         # pylint: disable=arguments-differ
-        self.be_cosmo = Nc.HICosmo.new_from_name(Nc.HICosmo, "NcHICosmoDECpl{'massnu-length':<1>}")
+        self.be_cosmo = Nc.HICosmoDECpl(massnu_length=1)
         self.be_cosmo.omega_x2omega_k()
         self.be_cosmo.param_set_by_name("w0", -1.0)
         self.be_cosmo.param_set_by_name("w1", 0.0)
@@ -136,6 +138,10 @@ def _update_vec_funcs(self):
         self._eval_sigma_crit_core = np.vectorize(
             lambda z_len, z_src: (self.smd.sigma_critical(self.be_cosmo, z_src, z_len, z_len))
         )
+        if self.dist is not None:
+            self.dist.prepare_if_needed(self.be_cosmo)
+        if self.smd is not None:
+            self.smd.prepare_if_needed(self.be_cosmo)
 
     def _get_rho_c(self, z):
         return (