Merge branch 'main' into issue/401/use_qp

.github/workflows/build_check.yml

@@ -12,45 +12,47 @@ jobs:
 
     steps:
     - uses: actions/checkout@v4
-    - uses: conda-incubator/setup-miniconda@v2
-    - name: Install prereq using conda
+    - uses: conda-incubator/setup-miniconda@v3
+    - name: Install prereq and NumCosmo using conda
       run: |
         echo "$CONDA/bin" >> $GITHUB_PATH
-        conda install -c conda-forge gobject-introspection pygobject
+        conda install -c conda-forge gobject-introspection pygobject numcosmo cmake swig setuptools_scm sphinx sphinx_rtd_theme nbconvert pandoc ipython
     - name: Install prereq using pip
       run: |
         pip install -r requirements.txt
     - name: Install the package
       run: |
         pip install .
-    - name: Install NumCosmo from conda-forge
-      run: |
-        conda install -c conda-forge numcosmo
+# temporarily moved up due to libarchive issue - see PR 620
+#    - name: Install NumCosmo from conda-forge
+#      run: |
+#        conda install -c conda-forge numcosmo
     - name: Install cluster_toolkit from source
       run: |
         git clone https://github.com/tmcclintock/cluster_toolkit.git
         cd cluster_toolkit
         pip install .
     - name: Install CCL from source
       run: |
-        conda install -c conda-forge cmake swig --override-channels
+        # temporarily moved up due to libarchive issue - see PR 620
+        #conda install -c conda-forge cmake swig setuptools_scm
         git clone https://github.com/LSSTDESC/CCL
         cd CCL
-        git checkout v3.0.0
-        pip install .
+        pip install --no-use-pep517 .
     - name: Analysing the code with pylint
       run: |
         pip install pylint
-        pylint clmm
+        pylint clmm --ignored-classes=astropy.units
     - name: Run the unit tests
       run: |
         pip install pytest pytest-cov
         pytest tests/ --ignore=cluster_toolkit/tests --cov=clmm/
       env:
         DISPLAY: test
-    - name: Install Sphinx prereq
-      run: |
-        conda install -c conda-forge sphinx sphinx_rtd_theme nbconvert pandoc ipython ipython_genutils
+# temporarily moved up due to libarchive issue - see PR 620
+#    - name: Install Sphinx prereq
+#      run: |
+#        conda install -c conda-forge sphinx sphinx_rtd_theme nbconvert pandoc ipython ipython_genutils
     - name: Run the docs
       run: |
         make -C docs/ html

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 2.7.1.dev10+gf81b59a4 and 3),
-NumCosmo (v0.15 or later),
+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

@@ -1,4 +1,3 @@
-
 # CLMM
 [![Build and Check](https://github.com/LSSTDESC/CLMM/workflows/Build%20and%20Check/badge.svg)](https://github.com/LSSTDESC/CLMM/actions?query=workflow%3A%22Build+and+Check%22)
 [![Coverage Status](https://coveralls.io/repos/github/LSSTDESC/CLMM/badge.svg?branch=main)](https://coveralls.io/github/LSSTDESC/CLMM?branch=main)
@@ -23,8 +22,8 @@ link to this repository: https://github.com/LSSTDESC/CLMM. Please follow the gui
 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)  
+- [SciPy](https://scipy.org/) (v1.6 or later)
+- [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)
 
@@ -36,7 +35,7 @@ For the theoretical predictions of the signal, CLMM relies on existing libraries
 
 - [cluster-toolkit](https://cluster-toolkit.readthedocs.io/en/latest/)
 - [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)
+- [NumCosmo](https://numcosmo.github.io/) (v0.19 or later)
 
 
 (See the [INSTALL documentation](INSTALL.md) for more detailed installation instructions.)
@@ -91,8 +90,8 @@ 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.
 
 
@@ -110,6 +109,7 @@ the `CCL` publication be cited.  See details
 The `Cluster Toolkit` documentation can be found
 [here](https://cluster-toolkit.readthedocs.io/en/latest/#).
 
+The data for the notebook test_coordinate.ipynb is available at https://www.dropbox.com/scl/fo/dwsccslr5iwb7lnkf8jvx/AJkjgFeemUEHpHaZaHHqpAg?rlkey=efbtsr15mdrs3y6xsm7l48o0r&st=xb58ap0g&dl=0
 
 # Contributing to CLMM <a name="contributing"></a>
 
@@ -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__ = "1.10.0"
+__version__ = "1.13.0"

clmm/clusterensemble.py

@@ -7,6 +7,7 @@
 
 from .gcdata import GCData
 from .dataops import make_stacked_radial_profile
+from .utils import DiffArray
 
 
 class ClusterEnsemble:
@@ -27,7 +28,7 @@ class ClusterEnsemble:
 
         * "tan_sc" : tangential component computed with sample covariance
         * "cross_sc" : cross component computed with sample covariance
-        * "tan_jk" : tangential component computed with bootstrap
+        * "tan_bs" : tangential component computed with bootstrap
         * "cross_bs" : cross component computed with bootstrap
         * "tan_jk" : tangential component computed with jackknife
         * "cross_jk" : cross component computed with jackknife
@@ -46,7 +47,7 @@ def __init__(self, unique_id, gc_list=None, **kwargs):
         else:
             raise TypeError(f"unique_id incorrect type: {type(unique_id)}")
         self.unique_id = unique_id
-        self.data = GCData(meta={"bin_units": None})
+        self.data = GCData(meta={"bin_units": None, "radius_min": None, "radius_max": None})
         if gc_list is not None:
             self._add_values(gc_list, **kwargs)
         self.stacked_data = None
@@ -198,6 +199,9 @@ def add_individual_radial_profile(
         """
         cl_bin_units = profile_table.meta.get("bin_units", None)
         self.data.update_info_ext_valid("bin_units", self.data, cl_bin_units, overwrite=False)
+        for col in ("radius_min", "radius_max"):
+            value = DiffArray(profile_table[col])
+            self.data.update_info_ext_valid(col, self.data, value, overwrite=False)
 
         cl_cosmo = profile_table.meta.get("cosmo", None)
         self.data.update_info_ext_valid("cosmo", self.data, cl_cosmo, overwrite=False)
@@ -248,9 +252,14 @@ def make_stacked_radial_profile(self, tan_component="gt", cross_component="gx",
             [self.data[tan_component], self.data[cross_component]],
         )
         self.stacked_data = GCData(
-            [radius, *components],
-            meta=self.data.meta,
-            names=("radius", tan_component, cross_component),
+            [
+                self.data.meta["radius_min"].value,
+                self.data.meta["radius_max"].value,
+                radius,
+                *components,
+            ],
+            meta={k: v for k, v in self.data.meta.items() if k not in ("radius_min", "radius_max")},
+            names=("radius_min", "radius_max", "radius", tan_component, cross_component),
         )
 
     def compute_sample_covariance(self, tan_component="gt", cross_component="gx"):

clmm/cosmology/ccl.py

@@ -76,29 +76,29 @@ def _get_param(self, key):
         return value
 
     def _get_Omega_m(self, z):
-        a = self.get_a_from_z(z)
+        a = self._get_a_from_z(z)
         return ccl.omega_x(self.be_cosmo, a, "matter")
 
     def _get_E2(self, z):
-        a = self.get_a_from_z(z)
+        a = self._get_a_from_z(z)
         return (ccl.h_over_h0(self.be_cosmo, a)) ** 2
 
     def _get_E2Omega_m(self, z):
-        a = self.get_a_from_z(z)
+        a = self._get_a_from_z(z)
         return ccl.omega_x(self.be_cosmo, a, "matter") * (ccl.h_over_h0(self.be_cosmo, a)) ** 2
 
     def _get_rho_m(self, z):
         # total matter density in physical units [Msun/Mpc3]
-        a = self.get_a_from_z(z)
+        a = self._get_a_from_z(z)
         return ccl.rho_x(self.be_cosmo, a, "matter", is_comoving=False)
 
     def _get_rho_c(self, z):
-        a = self.get_a_from_z(z)
+        a = self._get_a_from_z(z)
         return ccl.rho_x(self.be_cosmo, a, "critical", is_comoving=False)
 
     def _eval_da_z1z2_core(self, z1, z2):
-        a1 = np.atleast_1d(self.get_a_from_z(z1))
-        a2 = np.atleast_1d(self.get_a_from_z(z2))
+        a1 = np.atleast_1d(self._get_a_from_z(z1))
+        a2 = np.atleast_1d(self._get_a_from_z(z2))
         if len(a1) == 1 and len(a2) != 1:
             a1 = np.full_like(a2, a1)
         elif len(a2) == 1 and len(a1) != 1:
@@ -110,10 +110,10 @@ def _eval_da_z1z2_core(self, z1, z2):
         return res
 
     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)
+        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_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))
+        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 (