Joss

.github/workflows/paper.yml → .github/workflows/draft-pdf.yml

@@ -1,23 +1,28 @@
-on: [push]
+name: Draft PDF
+on:
+  push:
+    paths:
+      - paper/**
+      - .github/workflows/draft-pdf.yml
 
 jobs:
   paper:
     runs-on: ubuntu-latest
     name: Paper Draft
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
       - name: Build draft PDF
         uses: openjournals/openjournals-draft-action@master
         with:
           journal: joss
           # This should be the path to the paper within your repo.
           paper-path: paper/paper.md
       - name: Upload
-        uses: actions/upload-artifact@v1
+        uses: actions/upload-artifact@v3
         with:
           name: paper
           # This is the output path where Pandoc will write the compiled
           # PDF. Note, this should be the same directory as the input
           # paper.md
-          path: paper/paper.pdf
+          path: paper/paper.pdf

.github/workflows/run_tests.yml

@@ -9,15 +9,20 @@ jobs:
       max-parallel: 5
 
     steps:
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
     - uses: actions/checkout@v4
-    - uses: actions/setup-python@v5
+    - uses: conda-incubator/setup-miniconda@v3
       with:
-        python-version: '3.11'
-    - run: |
-        # $CONDA is an environment variable pointing to the root of the miniconda directory
-        echo $CONDA/bin >> $GITHUB_PATH
-    - run: python -m pip install --upgrade pip setuptools wheel
-    - run: python -m pip install .
-    - run: |
+        activate-environment: base-test
+    - name: Install dependencies
+      run: |
+        # conda env update --file=../environment.yml --name=base-test
+        python -m pip install colossus pixell camb
+        python -m pip install .
+    - name: Test with pytest
+      run: |
         conda install pytest
         pytest

README.md

@@ -2,22 +2,45 @@
 
 Code for producing fast simulations of the SZ effect for galaxy halos of varying redshift and mass, based on average thermal pressure profile fits from [Battaglia et al. 2012](https://ui.adsabs.harvard.edu/abs/2012ApJ...758...75B/abstract). Simulated submaps can include tSZ signal from these halos, simulated CMB, instrument beam convolution and white noise.
 
-## Installation 
+## Code Overview
+
+The code is structured as depicted here: ![DeepSZSim workflow](paper/figures/DeepSZSim_Workflow.png)
+The CMB simulations are handled by [DeepCMBSim](https://www.github.com/deepskies/deepcmbsim), based on [CAMB](https://camb.info), and further by [pixell](https://github.com/simonsobs/pixell). The SZ cluster simluations are done in `make_sz_cluster.py` and instrumental effects are added in `filters.py` and `noise.py`.  
+
+## Quickstart
+
+### Installation 
 
 We provide an environment specification file for `conda` or `mamba` users at `environment.yml`, which will produce a new virtual environment called `szsims` with appropriate versions of major python packages. With `conda`, this environment is created by `conda env create -f environment.yml`. With `micromamba` the `env` is omitted and a new environment is instead created with `micromamba create -f environment.yml`.
 
 The simulated CMB signal relies on `camb` and `pixell`, cosmology relies on `colossus`, and utilities for saving rely on `h5py`. These are specified in the `pyproject.toml` file.
 
 From the top-level directory, you can do `pip install .` to install the package.
 
-## Usage
+### Usage
 
 The usage of this code is documented in `notebooks/demo_simulation.ipynb`. A detailed walkthrough of the functions available in this code is in `notebooks/demo_full_pipeline.ipynb`.
 
 A full list of potential inputs is documented in `settings/config.yaml` and you can edit `settings/inputdata.yaml` to reflect your desired simulation settings.  
 
 `dm_halo_dist.py` generates an array of mass and redshift. The functions in `make_sz_cluster.py` create pressure profiles, Compton-y, and SZ signal maps for a halo of a given mass and redshift, and produces the final simulated submaps. These submaps contain simulated CMB and simple instrument beam convolution from `simtools.py` and white noise from `noise.py`. Plotting tools are provided in `visualization.py`. Simulations of a large suite of clusters can be achieved easily with `simclusters.py`.
 
+### Example
+
+Let's say you wanted to produce 100 mock halos distributed across the redshift range 0.2<z<0.4 and with masses in the range 1e14<M200<1e15. To generate these halos and produce their simulated maps with SZ signal (along with CMB signal and noise parameters as specified in `Settings/inputdata.yaml`) you would call
+```commandline
+import deepszsim as dsz
+tc0 = dsz.simulate_clusters(halo_params_dict={
+                            'zmin':0.2, 'zmax':0.5,
+                            'm200min_SM':1e14, 'm200max_SM':1e15
+                            },
+                            num_halos=100)
+tc0.get_T_maps()
+```
+The clusters and their maps are now in a dictionary which is in a `clusters` attribute of the class instance `tc0`.
+
+To access the clusters in this set, you can refer to the cluster ID, which itself is obtained from the first five digits of the cluster mass and two digits of the cluster redshift, followed by six random digits. For example, to access a dictionary of the maps and the parameters describing the eleventh cluster, you would do `tc0.clusters[tc0.id_list[11]]`. Alternately, to get the ''final'' temperature map (with noise) for the eleventh cluster, we also provide a convenience function: `tc0.ith_T_map(11)` is the same as `tc0.clusters[tc0.id_list[11]]['maps']['final_map']`.
+
 ## Citation
 
 If you use this code in your research, please cite this GitHub repo and our JOSS paper. Please also make use of the citation instructions for `camb` provided [here](https://camb.info).