| [data_download_cbcflow.py](https://git.ligo.org/srashti.goyal/lensid-ml-o4/-/blob/master/data_download_preparation/data_download_cbcflow.py) | Script for downloading events info from CBCFlow (path loaded on CIT), skymaps(.fits) from GraceDB and strain data from ligo servers using GWpy. Note: needs valid ligo key path for accessing non-public data in Gracedb., eg: `/tmp/x509up\\\\\\\*` Raw data on CIT is in `/home/srashti.goyal/lensid_O4/data_download_preparation/O4a_events_data`| | | ------- | |
| [data_prepare.py](https://git.ligo.org/srashti.goyal/lensid-ml-o4/-/blob/master/data_download_preparation/data_prepare.py) | Prepare cartesian skymaps and qtransforms for O4a the real events. Also filters events compatible with lensid (BBHs) and prepares a dataframe. Note: The prepared inputs are saved on CIT in `/home/srashti.goyal/lensid_O4/data_download_preparation/O4a_lensid_inputs` | | | --- | |
| [2vs3det.ipynb](https://git.ligo.org/srashti.goyal/lensid-ml-o4/-/blob/master/retraining_for_O4/2vs3det.ipynb) | Skymaps ML performance for HL v/s HLV | | | -------------- |
| [PSD_plots.ipynb](https://git.ligo.org/srashti.goyal/lensid-ml-o4/-/blob/master/retraining_for_O4/PSD_plots.ipynb) | Comparison of the PSDs used for training and testing in O3 and O4 etc. *will be changed* | | | -------------- |
<details><summary>**yet to finalise**</summary>
| Notebook | Short description | Status | Comment | Reviewed |
| [train_test_pars.ipynb](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/review/train_test_pars.ipynb) | Notebook having plots of injection parameters for training, testing and O3a sets that are used. | | | -------------- |
| [pop_datasets.ipynb](https://git.ligo.org/srashti.goyal/lensid-ml-o4/-/blob/master/retraining_for_O4/pop_datasets.ipynb) | Notebook having plots of injection parameters for training and testing. | | | -------------- |
| Config | ML models and config for production runs | | | -------------- |
| PSDs | PSD used for training and testing | | | -------------- |
| ML_train_test_optimise_fpp | ML models benchmark performance and background estimations. | | | -------------- |
...
...
@@ -133,5 +135,6 @@ The review call happen on Wednesdays 1 PM CEST/ 4:30 PM IST virtual IFPA room: h
## Action items
- [ ] Plot ROCs varying the SNRs, chirp masses etc.
- [ ] Update result review page with the new scripts.
- [ ] Update the result review page with the new scripts.
