Extended run instructions

Re-implemented run-instructions for swarm plot and a few clarification points

README.md

@@ -130,7 +130,7 @@ python3 find_conformers.py -u "O34926" \
 
 ---
 
-#### Option 3) Render distance difference maps only
+#### Option 3) Render all distance difference maps
 
 2D histograms (heatmaps) can be rendered and saved for each CA distance difference matrix by specifying the save directory using the `-o` flag:
 
@@ -160,6 +160,8 @@ python3 find_conformers.py -u "O34926" \
     -o benchmark_data/examples/O34926/O34926_distance_difference_maps/
 ```
 
+**CAUTION**: This can be slow for large datasets.
+
 ---
 
 #### Option 4) Render dendrogram only
@@ -196,7 +198,7 @@ python3 find_conformers.py -u "O34926" \
 
 ---
 
-<!-- #### Option 5) Render swarm plot
+#### Option 5) Render swarm plot
 
 The scores generated between pairwise structure comparisons can be plotted as a swarm plot by parsing the `-w` flag:
 
@@ -223,10 +225,11 @@ python3 find_conformers.py -u "O34926" \
     -m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
     -c benchmark_data/examples/O34926/O34926_ca_distances \
     -d benchmark_data/examples/O34926/O34926_distance_differences/ \
+    -s benchmark_data/examples/O34926/O34926_cluster_results/ \
     -w benchmark_data/examples/O34926/O34926_cluster_results/ png svg
 ```
 
------- -->
+------
 
 #### Option 6) Include AlphaFold Database structure when generating CA and distance difference matrices
 
@@ -367,7 +370,7 @@ This is the recommended option for most users who need to run the CLI applicatio
 docker build -t protein-cluster-conformers .
 ```
 
-## Run the Docker container
+### Run the Docker container
 
 When running the container, all output files are saved in the `/data/output` directory. To ensure the output files are saved to your machine, mount a local directory to the container using the `-v` flag, like in the example below. Specify input chains using the `-m` (and the `-u` flag for the UniProt accession) as detailed in the CLI section above, after the image name (`protein-cluster-conformers` in this case). The `-c`, `-d` and `-s` arguments are predefined in the Docker file, but are set sub-directories in the now-mounted `/data/output` directory.
 
@@ -404,6 +407,23 @@ Inside the Docker container, the application scripts are saved to `/app` and dat
 
 The additional optional flags from the CLI instructions above can be parsed in after the image name. 
 
+## Run Instructions: Python API
+
+To import the package into your own Python code, refer to `/tutorials/instructions.ipynb` for a step-by-step guide on how to use the package.
+
+The package is pip-installable, and can be imported into your own Python code using:
+
+```shell
+pip install protein-cluster-conformers
+```
+
+Then import the package into your Python code:
+
+```python
+from cluster_conformers import ClusterConformers
+...
+```
+
 ## Contributing
 
 Install developer dependencies: