class_sz implementation for cibxkappa

.github/workflows/testing.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     defaults:
       run:
-        shell: bash -l {0}    
+        shell: bash -l {0}
     strategy:
       fail-fast: false
       matrix:
@@ -27,10 +27,11 @@ jobs:
 
     - name: install reqs
       run: |
-        conda install pip compilers pytest pyccl cython
+        conda install pip compilers pytest pyccl cython gsl
+        pip install gsl
         pip install cobaya
 
-      env:  
+      env:
         MATRIX_OS: ${{ matrix.os }}
 
     - name: Install classy
@@ -44,6 +45,17 @@ jobs:
       env:
         MATRIX_OS: ${{ matrix.os }}
 
+    - name: Install classy_sz
+      run: |
+        if [[ ${MATRIX_OS} == "macos-latest" ]]; then
+          . ci_scripts/install_class_sz_osx.sh
+        else
+          . ci_scripts/install_class_sz_linux.sh
+        fi
+        python -c "import classy_sz; print(classy_sz)"
+      env:
+        MATRIX_OS: ${{ matrix.os }}
+
     - name: install
       run: |
         pip install .

.gitignore

@@ -102,3 +102,42 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+
+# ymap
+.DS_Store
+soliket/ymap/chains/*
+soliket/ymap/run_scripts/*
+soliket/ymap/notebooks/*
+#soliket/ymap/input_files/
+soliket/ymap/input_files/total-planck-collab-15_benchmark_montepython-3.3_evaluate_trispectrum.input.yaml
+soliket/ymap/input_files/total-planck-collab-15_benchmark_montepython-3.3.input.yaml
+soliket/ymap/input_files/sz_ps_template_evaluate_trispectrum_and_sz_template.yaml
+
+#cluster counts
+dndzdm_bins_m_center.txt
+dndzdy.txt
+dndzdy_bins_y_center.txt
+dndzdy_bins_z_center.txt
+soliket/sz_binned_cluster_counts/chains
+soliket/sz_binned_cluster_counts/data
+
+soliket/binned_clusters/data
+
+soliket/clusters/data
+soliket/binned_clusters/data/so/MFMF_WebSkyHalos_A10tSZ_3freq_tiles/MFMF_WebSkyHalos_A10tSZ_3freq_tiles_mass.fits
+soliket/binned_clusters/data/so/MFMF_WebSkyHalos_A10tSZ_3freq_tiles/selFn/tileAreas.txt
+soliket/cosmopower/chains
+soliket/binned_clusters/chains/test.updated.yaml
+soliket/binned_clusters/chains/test_camb.updated.yaml
+
+soliket/binned_clusters/chains/*
+soliket/clusters/chains/*
+soliket/clusters/notebooks/*
+
+soliket/cosmopower/data
+*.yaml
+soliket/cosmopower/covmats/desy1_joint_cosmopower_301122.covmat
+soliket/cosmopower/covmats/lensing_H0wide_harlap_emunotauzpk_omcwide.covmat
+soliket/cosmopower/covmats/desy1_joint_plancklensing2018_lenspriors_BAO_cosmopower.covmat
+
+yg*

README.md

@@ -12,96 +12,6 @@ git clone https://github.com/simonsobs/soliket
 cd soliket
 pip install -e .
 ```
-You will also need to either run
-```
-pip install camb
-```
-or, for a fuller cobaya install:
-```
-cobaya-install cosmo -p /your/path/to/cobaya/packages
-```
-To run tests, you will also need the original LAT_MFlike package:
-```
-pip install git+https://github.com/simonsobs/lat_mflike
-```
-Then, you can run tests with 
-```
-pip install pytest
-pytest -v .
-```
-
-Please raise an issue if you have trouble installing or any of the tests fail.
-
-## Contains
-
-This repo currently implements the following specific likelihoods:
-
-* `MFLike`: the SO LAT multi-frequency TT-TE-EE power spectrum likelihood. (Adapted from, and tested against, the original implementation [here](https://github.com/simonsobs/lat_mflike)).
-* `ClusterLikelihood`: An SZ-cluster count likelihood based on the original ACT SZ clusters likelihood.
-* `LensingLikelihood`: Lensing power-spectrum likelihood, adapted from [here](https://github.com/simonsobs/so-lenspipe/blob/6abdc185764894cefa76fd4666243669d7e8a4b0/bin/SOlikelihood/cobayalike.py#L80).
-* `LensingLiteLikelihood`: A no-frills, simple $\chi^2$ lensing power spectrum.
-
-## Extending
-
-Please see [these guidelines](guidelines.md) for instructions on bringing a new likelihood into **soliket**.
-
-## Usage
-
-These likelihoods are designed for direct use with **cobaya**.  This means that 
-they may be specified directly when creating a **cobaya** `Model`.  E.g., if
-you wanted to compute the likelihood of the simulated lensing data, you could do the following:
-
-```python
-
-from cobaya.yaml import yaml_load
-from cobaya.model import get_model
-
-info_yaml = """
-debug: True
-
-likelihood:
-  soliket.LensingLiteLikelihood:
-    sim_number: 1
-    stop_at_error: True
-
-params:
-  # Sampled
-  logA:
-    prior:
-      min: 2.6
-      max: 3.5
-    proposal: 0.0036
-    drop: True
-    latex: \log(10^{10} A_\mathrm{s})
-  As:
-    value: "lambda logA: 1e-10*np.exp(logA)"
-    latex: A_\mathrm{s}
-  ns:
-    prior:
-      min: 0.9
-      max: 1.1
-    proposal: 0.0033
-    latex: n_\mathrm{s}
-
-
-theory:
-  camb:
-    stop_at_error: False
-    extra_args:
-      lens_potential_accuracy: 1
-
-"""
-
-info = yaml_load(info_yaml)
-model = get_model(info)
-```
-The likelihood could then be either directly computed as 
-```python
-model.loglike(dict(logA=3.0, ns=0.98))
-```
-and used outside of **cobaya** (e.g., directly passed to **emcee** or some other
-sampler or optimizer), or this same YAML setup (with an additional 'sampler' block specified) 
-could be used as input to `cobaya-run` to have **cobaya** manage the sampling.
 
-For more information on how to use **cobaya**, check out its [documentation](http://cobaya.readthedocs.io).
 
+This is a minimal soliket version to run with cosmopower emulators. 

ci_scripts/install_class_sz_linux.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+
+sudo apt-get install libgsl-dev
+cd /home/runner/work/SOLikeT/SOLikeT/
+wget ftp://ftp.gnu.org/gnu/gsl/gsl-2.6.tar.gz
+tar -zxvf gsl-2.6.tar.gz
+cd gsl-2.6
+./configure --prefix=/home/runner/work/SOLikeT/SOLikeT/gsl-2.6
+make
+make install
+LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/runner/work/SOLikeT/SOLikeT/gsl-2.6/lib/
+export LD_LIBRARY_PATH
+cd ..
+ls /home/runner/work/SOLikeT/SOLikeT/gsl-2.6/lib
+git clone https://github.com/borisbolliet/class_sz.git
+cd class_sz
+make -j4
+
+# at this point the make file leaves you in the python dir
+cd ..

ci_scripts/install_class_sz_osx.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+export CONDA_BUILD_SYSROOT=/
+
+rm -rf class_sz
+git clone --depth=1000 https://github.com/borisbolliet/class_sz.git
+cd class_sz
+
+sed -i.bak -e 's/^CC/#CC/g' Makefile
+sed -i.bak -e 's/^OPTFLAG =/OPTFLAG = ${CFLAGS} ${LDFLAGS}/g' Makefile
+sed -i.bak -e 's/^#CCFLAG +=/CCFLAG +=/g' Makefile
+sed -i.bak -e 's/^#CCFLAG =/CCFLAG =/g' Makefile
+
+make -j4
+
+# at this point the make file leaves you in the python dir
+cd ..

setup.py

@@ -8,25 +8,27 @@
     version="0.0",
     description="SO Likelihoods & Theories",
     zip_safe=False,
-    packages=["soliket", "soliket.tests", "soliket.clusters"],
+    # packages=["soliket", "soliket.tests", "soliket.clusters", "soliket.ymap"],
+    packages=["soliket"],#, "soliket.tests","soliket.clusters","soliket.ymap"],
     package_data={
         "soliket": [
             "*.yaml",
             "*.bibtex",
             # "data/simulated*/*.txt",
-            "clusters/data/*",
-            "clusters/data/selFn_equD56/*",
-            "lensing/data/*.txt",
+            # "clusters/data/*",
+            # "clusters/data/selFn_equD56/*",
+            # "ymap/data/*.txt"
+
         ]
     },
     install_requires=[
         "astropy",
         "scikit-learn",
         "cobaya",
         "sacc",
-        "pyccl",
-        "fgspectra @ git+https://github.com/simonsobs/fgspectra@master#egg=fgspectra",
-        "mflike @ git+https://github.com/simonsobs/lat_mflike"
+        # "pyccl",
+        # "fgspectra @ git+https://github.com/simonsobs/fgspectra@master#egg=fgspectra",
+        # "mflike @ git+https://github.com/simonsobs/LAT_MFLike"
     ],
     test_suite="soliket.tests",
 )

soliket/__init__.py

@@ -1,12 +1,12 @@
 from .lensing import LensingLiteLikelihood, LensingLikelihood
 from .gaussian import GaussianLikelihood, MultiGaussianLikelihood
-from .ps import PSLikelihood, BinnedPSLikelihood
-from .clusters import ClusterLikelihood
-from .mflike import MFLike
-try:
-    import pyccl as ccl
-    from .ccl import CCL
-    from .cross_correlation import CrossCorrelationLikelihood
-except ImportError:
-    print('Skipping CCL module as pyCCL is not installed')
-    pass
+# from .ps import PSLikelihood, BinnedPSLikelihood
+# from .clusters import ClusterLikelihood
+# from .mflike import MFLike
+# from .ccl import CCL
+# from .ymap.ymap_ps import SZLikelihood, SZForegroundTheory
+## from .sz_binned_cluster_counts.binned_cc import binned_cc_likelihood
+# from .classy_cibxkappa import classy_cibxkappa
+from .cibxlensing import CIBxKAPPA_Likelihood
+# from .yg.galaxy_x_galaxy import GXG_Likelihood
+# from .yg.y_x_galaxy import YXG_Likelihood