Update the intro a bit

Author: jeromekelleher

docs/api.md

@@ -1,3 +1,5 @@
+(sec_python_api)=
+
 # Python API
 
 This page documents the public Python API exposed by ``sc2ts``.

docs/intro.md

@@ -6,105 +6,17 @@ at pandemic scale.
 It consists of:
 
 1. A CLI-driven method to infer ARGs from SARS-CoV-2 data.
-2. A lightweight wrapper around the :mod:`tskit` Python APIs, specialised
+2. A lightweight wrapper around the {mod}`tskit` Python APIs, specialised
    for the output of sc2ts and enabling efficient node metadata access.
-3. A lightweight wrapper around :mod:`zarr` for convenient access to the
+3. A lightweight wrapper around [Zarr](https://zarr.dev) for convenient access to the
    Viridian dataset (alignments and metadata) in VCF Zarr format.
 
-The underlying methods are described in the sc2ts pre-print:
-<https://www.biorxiv.org/content/10.1101/2023.06.08.544212v2>.
+The underlying methods are described in the sc2ts [preprint](
+<https://www.biorxiv.org/content/10.1101/2023.06.08.544212v2>).
 
-Most users will run sc2ts via the command line interface,
-which drives inference and postprocessing steps (see the
-{ref}`CLI documentation <sc2ts_sec_cli>`). The Python API is intended for
-working with tree sequences and datasets produced by sc2ts (see the
-{ref}`Python API reference <api>`).
+Most users will use the {ref}`sec_python_api` to perform {ref}`sec_arg_analysis`
+on the sc2ts inferred ARG or {ref}`sec_alignments_analysis` on the
+Zarr-formatted Viridian dataset distributed on Zenodo.
 
-For an overview and examples, see the project README and associated
-notebooks in the repository root.
-
-## Installation
-
-Install sc2ts from PyPI:
-
-```sh
-python -m pip install sc2ts
-```
-
-This installs the minimal requirements for the analysis and dataset APIs.
-To run inference from the command line, install the optional inference
-dependencies:
-
-```sh
-python -m pip install 'sc2ts[inference]'
-```
-
-## Quick start: ARG analysis
-
-To compute summary dataframes for nodes and mutations in an inferred ARG,
-you can load an sc2ts tree sequence and call the analysis helpers. For
-example, download the sc2ts paper ARG from Zenodo:
-
-```sh
-curl -O https://zenodo.org/records/17558489/files/sc2ts_viridian_v1.2.trees.tsz
-```
-
-and then:
-
-```python
-import sc2ts
-import tszip
-
-ts = tszip.load("sc2ts_viridian_v1.2.trees.tsz")
-df_node = sc2ts.node_data(ts)
-df_mutation = sc2ts.mutation_data(ts)
-```
-
-See the {ref}`Python API reference <api>` for full details of these
-functions.
-
-## Quick start: CLI inference
-
-To run inference locally using the example Viridian dataset and config:
-
-1. Install the inference extras (if you have not already):
-
-   ```sh
-   python -m pip install 'sc2ts[inference]'
-   ```
-
-2. Download the Viridian dataset in VCF Zarr format:
-
-   ```sh
-   curl -O https://zenodo.org/records/16314739/files/viridian_mafft_2024-10-14_v1.vcz.zip
-   ```
-
-3. Run primary inference using the CLI and the example config in this repo:
-
-   ```sh
-   python -m sc2ts infer example_config.toml --stop=2020-02-02
-   ```
-
-   This will produce a series of `.ts` files and a match database in the
-   output directory specified by the config (see the README for details).
-
-4. Postprocess and generate an analysis-ready ARG:
-
-   ```sh
-   python -m sc2ts postprocess -vv \
-       --match-db example_inference/ex1.matches.db \
-       example_inference/ex1/ex1_2020-02-01.ts \
-       example_inference/ex1_2020-02-01_pp.ts
-
-   python -m sc2ts minimise-metadata \
-       -m strain sample_id \
-       -m Viridian_pangolin pango \
-       example_inference/ex1_2020-02-01_pp.ts \
-       example_inference/ex1_2020-02-01_pp_mm.ts
-   ```
-
-   The file `example_inference/ex1_2020-02-01_pp_mm.ts` can then be used
-   with the Python analysis APIs shown above.
-
-See the {ref}`CLI documentation <sc2ts_sec_cli>` for a complete listing of
-subcommands and options.
+Uses who wish to perform {ref}`sec_inference` use the
+{ref}`sc2ts_sec_cli`.