> For the complete documentation index, see [llms.txt](https://ez-lab.gitbook.io/cryobench/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ez-lab.gitbook.io/cryobench/cryobench-manual/calculating-fsc-metrics.md).
# Calculating FSC Metrics
We show here how to use CryoBench tools to generate FSCs between volumes reconstructed using our example methods and the ground truth volumes for our datasets.
{% hint style="info" %}
Before running these tools, make sure you have [installed CryoBench](/cryobench/getting-started/installation-instructions.md) and [generated reconstruction results](/cryobench/getting-started/running-reconstruction-models.md). Commands shown here use the same conda environments as described in the installation instructions!
{% endhint %}
## Calculating per-image FSCs
We have created the `cdrgn.py` script as an example of how to calculate FSCs between volumes reconstructed by a cryoDRGN model and the ground truth volumes for each particle class in a CryoBench dataset. We generate volumes by selecting a given number of images from the dataset and then using the model decoder to return the volume for the latent space coordinates assigned to each image.
Note that we use `19` for the epoch number even though [we ran training for 20 epochs](/cryobench/getting-started/running-reconstruction-models.md#cryodrgn-with-fixed-poses) as epoch numbers are 0-indexed:
```bash
$ conda activate cryodrgn_bench
(cryodrgn_bench)$ python ~/dev/CryoBench/metrics/fsc/cdrgn.py cryobench_input/IgG-1D/cryodrgn_fixed/ IgG-1D/gt_latents.pkl --gt-dir IgG-1D/vols/128_org/ --epoch 19 -o cryobench_output/IgG-1D/cryodrgn_fixed/ -n 100 --Apix 3.0
```
This will generate the following outputs in `cryobench_output/IgG-1D/cryodrgn_fixed/`:
1. Volumes `vol_xxx.mrc` generated by the cryoDRGN model for each of the chosen images (100 in this case due to `-n 100`)
2. A folder `fsc_no_mask/` containing FSC curves calculated between the above reconstructed volumes and ground truth volumes stored in `--gt-dir IgG-1D/vols/128_org/`. These are stored as `.txt` files with a column for 1/resolution and a column for FSC values:
```
pixres fsc
0.0 1.0
0.0078125 1.0
0.015625 0.9962001
0.0234375 0.9762605
0.03125 0.9209269
0.0390625 0.8759735
...
```
{% hint style="info" %}
This workflow is the same for cryoDRGN ab-initio outputs!
{% endhint %}
### Masked FSCs and reusing generated volumes
You can apply a mask to the volumes before computing FSCs by passing a `--mask` argument to the `cdrgn.py` script:
```bash
(cryodrgn_bench)$ python ~/dev/CryoBench/metrics/fsc/cdrgn.py cryobineinch_input/IgG-1D/cryodrgn_fixed/ IgG-1D/gt_latents.pkl --gt-dir IgG-1D/vols/128_org/ --epoch 19 --mask IgG-1D/init_mask/mask.mrc -o cryobench_output/IgG-1D/cryodrgn_fixed_mask/ -n 100 --Apix 3.0
```
This will generate the same outputs as above — but the new FSCs will be placed in a subfolder named, in this case, `fsc_mask/`. In general, the `_mask` suffix will be replaced by the filename of the `.mrc` mask used.
You can also use `cdrgn.py` with an `-o` output folder that already contains generated volumes to recalculate FSCs with a mask, thus putting multiple sets of FSCs in the same output path. The script will skip volume generation in this case and proceed directly to FSC calculation unless the `--overwrite` flag is used.
### Aligning volumes
Reconstructed volumes can be aligned to ground truth volumes using ChimeraX before calculating FSCs:
```bash
(cryodrgn_bench)$ python ~/dev/CryoBench/metrics/fsc/cdrgn.py cryobench_input/IgG-1D/cryodrgn_fixed/ IgG-1D/gt_latents.pkl --gt-dir IgG-1D/vols/128_org/ --epoch 19 --mask IgG-1D/init_mask/mask.mrc -o cryobench_output/IgG-1D/cryodrgn_fixed_aligned/ --serial-align -n 100 --Apix 3.0
```
This will produce the same output as above, but also with a `vol.x.txt` file for each generated volume containing a log of the alignment process.
Volumes can also be aligned in parallel by submitting jobs to a Slurm compute cluster (if available) using the `--parallel-align` flag, which is advisable is using a large number of images as aligning all volumes serially can be time-consuming!
## Plotting FSCs
To generate visualizations of the FSC curves at `cryobench_output/IgG-1D/cryodrgn_fixed/`:
```bash
(cryodrgn_bench)$ python ~/dev/CryoBench/metrics/fsc/plot_fsc.py cryobench_output/IgG-1D/cryodrgn_fixed/
```
This will plot the FSCs found in **all** `fsc_*` subfolders found in your CryoBench output folder, creating plots such as `cryodrgn_fixed/fsc_no_mask.png` and `cryodrgn_fixed/fsc_no_mask_means.png`:
## Calculating Per-Conformation FSCs for Other Methods
In our repository we have included scripts for calculating FSC metrics for the other CryoBench example reconstruction methods using the original per-conformation method under `metrics/fsc/old/per_conf/`; we can provide scripts for calculating FSCs using the newer per-image method upon request!
### DRGN-AI