Mass-gap-review.md

+# Mass Gap  
+## Step-wise installation of packages:
+1. Clone the repository in a desired directory.
+```
+git clone git@git.ligo.org:sushant.sharma-chaudhary/em-bright-gp.git
+cd em-bright-gp
+git checkout massgap
+
+```
+2. Create a anaconda environment using: `conda create -n {name} python=3.{8/9}`
+
+3. Activate the environment : `conda activate {name}`
+
+4. Install poetry using : `pip install poetry`
+
+5. Go to the em-bright cloned directory and install the package using : `poetry install`
+
+## Training classifier
+1. Change configurations for training in `etc/conf.ini` (if required)
+2. Creating condor jobs for training:
+``` 
+em_bright_dag_writer
+    -d {filename}.dag \
+    -w {output_dir} \
+    -i /home/sushant.sharma-chaudhary/O2-HL-rates-injections \ 
+    -c {CLONED_PROJECT_DIRECTORY}/etc/conf.ini \
+    -e $(dirname `which em_bright_dag_writer`) \
+    --mass-gap
+```
+3. Change directory to `{output_dir}`
+4. Submit condor jobs :
+` condor_submit_dag {filename}.dag`
+
+## Output files
+1. The `mass_gap.pickle` output is stored at `{output_dir}` directory
+2. The `parameter_sweep_mass_gap.png` is also stored at `{output_dir}` directory
+
+## Testing classifier on gstlal reconstructed data
+1. If you have previously installed em-bright-gp then: `rm ~/.ligo.em_bright`
+2. In the conda environment open python
+``` 
+from ligo.em_bright import em_bright
+em_bright.source_classification_genetic_prog({m1},{m2},{chi1},{chi2},{snr})
+Output
+(p_NS, p_EMB, p_massgap)
+```
+