CCL modes

environment.yml

@@ -27,6 +27,7 @@ dependencies:
   - pip:
     - autoclasstoc
     - cobaya
+    - isitgr
     - pygobject-stubs
   - portalocker
   - pybobyqa

examples/des_y1_3x2pt/README.md

@@ -1,38 +1,76 @@
 # DES Y1 3x2pt Analysis
 
-The code here will run a test sample for a Flat LCDM cosmology using either Cobaya or CosmoSIS. 
+The DES Y1 3x2pt analysis of the Dark Energy Survey (DES) Year 1 (Y1) data, focusing on
+**cosmic shear**, **galaxy-galaxy lensing**, and **galaxy clustering**. This repository
+demonstrates how to use the DES Y1 3x2pt data products to compute the likelihood with
+the following tools:
 
-## Running Cobaya
+- **Cobaya**  
+- **CosmoSIS**  
+- **NumCosmo**  
+
+## Likelihood Factories
+
+The **Firecrown** likelihoods can be customized through user-defined factories. In this
+example, the following factory files are provided:
 
-The Cobaya file `cobaya_evaluate.yaml` configures Cobaya to use the `des_y1_3x2pt.py` likelihood.
-Run it using:
+- `factory.py`: Factory for the DES Y1 3x2pt analysis.
+- `factory_PT.py`: Factory for the DES Y1 3x2pt analysis using perturbation theory.
 
-    cobaya-run cobaya_evaluate.yaml
+Alternatively, Firecrown’s **default factories** can be configured using experiment
+configuration files:
 
-This will produce the output files:
+- `experiment.yaml`: Standard configuration for the DES Y1 3x2pt analysis.
+- `pure_ccl_experiment.yaml`: Configuration for the DES Y1 3x2pt analysis in
+  **pure_ccl** mode.
+- `mu_sigma_experiment.yaml`: Configuration for **mu_sigma_isitgr** mode.
 
-    cobaya_evaluate_output.input.yaml
-    cobaya_evaluate_output.updated.yaml
-    cobaya_evaluate_output.updated.dill_pickle
-    cobaya_evaluate_output.1.txt
+Note that `mu_sigma_experiment.yaml` requires the
+[`isitgr`](https://github.com/mishakb/ISiTGR) package to be installed. This package is
+not installed by default in the Firecrown environment.
 
 ## Running CosmoSIS
 
-The pipeline configuration file `des_y1_3x2pt.ini` and the related `des_y1_3x2pt_values.ini` configure CosmoSIS to use the `des_y1_3x2pt.py` likelihood.
-Run this using:
+For each likelihood factory, a corresponding **CosmoSIS** pipeline configuration file is
+provided:
 
-    cosmosis des_y1_3x2pt.ini
+- `cosmosis/factory.ini`: Pipeline for the standard DES Y1 3x2pt analysis.
+- `cosmosis/factory_PT.ini`: Pipeline for the analysis with perturbation theory.
+- `cosmosis/pure_ccl.ini`: Pipeline for the analysis in **pure CCL** mode.
+- `cosmosis/mu_sigma.ini`: Pipeline for the analysis in **mu_sigma_isitgr** mode.
 
-This will produce the output to the screen, showing the calculated likelihood.
-It also creates the directory `des_y1_3x2pt_output` which is populated with the CosmoSIS datablock contents for the generated sample.
+### Example Command
 
-## Generating the `firecrown` Inputs
+To run the CosmoSIS pipeline, use the following command:
+
+```bash
+cosmosis cosmosis/factory.ini
+```
+
+## Running Cobaya
 
-Note, this code below only has to be run if you want to generate the firecrown input from the DES Y1 3x2pt data products.
-This file is stored at `examples/des_y1_3x2pt/des_y1_3x2pt_sacc_data.fits`.
-You do not have to make it yourself.
+For Cobaya, the pipeline configuration files are:
+
+- `cobaya/evaluate.yaml`: Pipeline for the standard DES Y1 3x2pt analysis.
+- `cobaya/evaluate_PT.yaml`: Pipeline for the analysis with perturbation theory.
+- `cobaya/evaluate_pure_ccl.yaml`: Pipeline for the analysis in **pure CCL** mode.
+- `cobaya/evaluate_mu_sigma.yaml`: Pipeline for the analysis in **mu_sigma_isitgr** mode.
+
+### Example Command
+
+To run the Cobaya pipeline, use the following command:
+
+```bash
+cobaya-run cobaya/factory.yaml
+```
+
+## Generating the `firecrown` Inputs
 
-To generate the `firecrown` inputs, first download the DES Y1 3x2pt data products [here](http://desdr-server.ncsa.illinois.edu/despublic/y1a1_files/chains/2pt_NG_mcal_1110.fits).
+There is already a `sacc_data.fits` file in this directory that contains the DES Y1
+3x2pt data products. If you want to regenerate the `firecrown` inputs, you can run
+`generate_des_data.py`. To generate the `firecrown` inputs, first download the DES Y1
+3x2pt data products
+[here](http://desdr-server.ncsa.illinois.edu/despublic/y1a1_files/chains/2pt_NG_mcal_1110.fits).
 
 Then run the script
 

examples/des_y1_3x2pt/cobaya_evaluate.yaml → examples/des_y1_3x2pt/cobaya/evaluate.yaml

@@ -10,7 +10,7 @@ theory:
 likelihood:
   des_y1_3x2pt:
     external: !!python/name:firecrown.connector.cobaya.likelihood.LikelihoodConnector ''
-    firecrownIni: des_y1_3x2pt.py
+    firecrownIni: ${FIRECROWN_DIR}/examples/des_y1_3x2pt/factory.py
     derived_parameters: 
     - TwoPoint__NumberCountsScale_lens0
     - TwoPoint__NumberCountsScale_lens1
@@ -182,7 +182,7 @@ params:
 sampler:
   evaluate: null
 stop_at_error: true
-output: cobaya_evaluate_output
+output: output
 packages_path: null
 test: false
 debug: false

examples/des_y1_3x2pt/cobaya_evaluate_PT.yaml → examples/des_y1_3x2pt/cobaya/evaluate_PT.yaml

@@ -10,7 +10,7 @@ theory:
 likelihood:
   des_y1_3x2pt:
     external: !!python/name:firecrown.connector.cobaya.likelihood.LikelihoodConnector ''
-    firecrownIni: des_y1_3x2pt_PT.py
+    firecrownIni: ${FIRECROWN_DIR}/examples/des_y1_3x2pt/factory_PT.py
 params:
   As:
     prior:
@@ -96,7 +96,7 @@ params:
 sampler:
   evaluate: null
 stop_at_error: true
-output: cobaya_evaluate_output_PT
+output: output_PT
 packages_path: null
 test: false
 debug: false

examples/des_y1_3x2pt/cobaya/evaluate_mu_sigma.yaml

@@ -0,0 +1,176 @@
+likelihood:
+  des_y1_3x2pt:
+    external: !!python/name:firecrown.connector.cobaya.likelihood.LikelihoodConnector ''
+    firecrownIni: firecrown.likelihood.factories.build_two_point_likelihood
+    build_parameters:
+      likelihood_config: ${FIRECROWN_DIR}/examples/des_y1_3x2pt/mu_sigma_experiment.yaml
+params:
+  sigma8:
+    prior:
+      min: 0.6
+      max: 1.0
+    ref: 0.8
+    proposal: 0.01
+  Omega_b: 0.05
+  Omega_c:
+    prior:
+      min: 0.05
+      max: 0.35
+    ref: 0.25
+    proposal: 0.01
+  Omega_k: 0.0
+  h: 0.682
+  Neff: 3.046
+  m_nu: 0.06
+  n_s: 0.971
+  w0: -1.0
+  wa: 0.0
+  T_CMB: 2.7255
+
+#   - MG Model
+  mg_musigma_mu: 
+     ref: 1.0
+     prior:
+        min: 0.8
+        max: 1.2
+  mg_musigma_sigma:
+       ref: 1.0
+       prior:
+         min: 0.8
+         max: 1.2
+  mg_musigma_c1: 1.0
+  mg_musigma_c2: 1.0
+  mg_musigma_lambda0: 1.0
+#   Likelihood params
+#   - IA model
+  ia_bias:
+     ref: 0.5
+     prior:
+        min: -5.0
+        max: +5.0
+  alphaz:
+     ref: 0.0
+     prior:
+        min: -5.0
+        max: +5.0
+
+#   - these parameters are fixed
+  z_piv: 0.62
+
+#   - linear bias for lenses
+  lens0_bias: 
+     ref: 1.4
+     prior:
+        min: 0.8
+        max: 3.0
+  lens1_bias: 
+     ref: 1.6
+     prior:
+        min: 0.8
+        max: 3.0
+  lens2_bias: 
+     ref: 1.6
+     prior:
+        min: 0.8
+        max: 3.0
+  lens3_bias: 
+     ref: 1.9
+     prior:
+        min: 0.8
+        max: 3.0
+  lens4_bias: 
+     ref: 2.0
+     prior:
+        min: 0.8
+        max: 3.0
+
+#   - photoz shifts for the lensing sources
+  src0_delta_z: 
+     ref: -0.001
+     prior:
+        dist: norm
+        loc: -0.001
+        scale: 0.016
+  src1_delta_z: 
+     ref: -0.019
+     prior:
+        dist: norm
+        loc: -0.019
+        scale: 0.013
+  src2_delta_z: 
+     ref: +0.009
+     prior:
+        dist: norm
+        loc: 0.009
+        scale: 0.011
+  src3_delta_z: 
+     ref: -0.018
+     prior:
+        dist: norm
+        loc: -0.018
+        scale: 0.022
+
+#   - photoz shifts for the lenses
+  lens0_delta_z:
+     ref: 0.001
+     prior:
+        dist: norm
+        loc: 0.001
+        scale: 0.008
+  lens1_delta_z: 
+     ref: 0.002
+     prior:
+        dist: norm
+        loc: 0.002
+        scale: 0.007
+  lens2_delta_z: 
+     ref: 0.001
+     prior:
+        dist: norm
+        loc: 0.001
+        scale: 0.007
+  lens3_delta_z: 
+     ref: 0.003
+     prior:
+        dist: norm
+        loc: 0.003
+        scale: 0.01
+  lens4_delta_z: 
+     ref: 0.0
+     prior:
+        dist: norm
+        loc: 0.0
+        scale: 0.01
+
+#   - shear errors
+  src0_mult_bias: 
+     ref: 0.0
+     prior:
+        dist: norm
+        loc: 0.012
+        scale: 0.023
+  src1_mult_bias: 
+     ref: 0.0
+     prior:
+        dist: norm
+        loc: 0.012
+        scale: 0.023
+  src2_mult_bias: 
+     ref: 0.0
+     prior:
+        dist: norm
+        loc: 0.012
+        scale: 0.023
+  src3_mult_bias: 
+     ref: 0.0
+     prior:
+        dist: norm
+        loc: 0.012
+        scale: 0.023
+sampler:
+  evaluate: null
+stop_at_error: true
+output: output_mu_sigma
+packages_path: null
+test: false
+debug: false