fast_nnt/
cli.rs

1use clap::{Args, Parser, Subcommand};
2
3use crate::{
4    ordering::OrderingMethod,
5    weights::{InferenceMethod, active_set_weights::NNLSParams},
6};
7
8#[derive(Parser, Debug)]
9#[command(version, about, long_about = None)]
10pub struct ProgramArgs {
11    #[command(subcommand)]
12    pub subcommand: ProgramSubcommand,
13    #[arg(
14        short,
15        long,
16        default_value = "1",
17        global = true,
18        help = "Number of threads to use."
19    )]
20    pub threads: usize,
21    #[arg(
22        short,
23        long,
24        default_value = "false",
25        conflicts_with = "quiet",
26        global = true
27    )]
28    pub verbose: bool,
29    #[arg(
30        short,
31        long,
32        default_value = "false",
33        conflicts_with = "verbose",
34        global = true
35    )]
36    pub quiet: bool,
37    #[arg(
38        short = 'd',
39        long,
40        default_value = "output",
41        global = true,
42        help = "Output directory"
43    )]
44    pub output_directory: String,
45}
46
47#[derive(Subcommand, Debug)]
48pub enum ProgramSubcommand {
49    #[clap(
50        name = "neighbour_net",
51        about = "Build a NeighbourNet split network from a distance matrix",
52        long_about = "Build a NeighbourNet split network (Bryant & Moulton, 2004) from a \
53distance matrix. Produces an implicit split network with a 2-D equal-angle \
54layout, written to a Nexus file."
55    )]
56    NeighborNet(NeighbourNetArgs),
57}
58
59#[derive(Args, Debug)]
60pub struct NeighbourNetArgs {
61    /// Input distance matrix file path
62    #[arg(short, long, help = "Input distance matrix file path", required = true)]
63    pub input: String,
64    #[arg(
65        short,
66        long,
67        help = "Output prefix for result files",
68        default_value = "output"
69    )]
70    pub output_prefix: String,
71    #[arg(
72        short = 'O',
73        long,
74        help = "The ordering algorithm to use to get splits cycle",
75        default_value = "multiway"
76    )]
77    pub ordering: OrderingMethod,
78    #[arg(
79        long,
80        help = "Split-weight inference algorithm",
81        default_value = "active-set"
82    )]
83    pub inference: InferenceMethod,
84    #[clap(flatten)]
85    pub nnls_params: NNLSParams,
86}
87
88impl Default for NeighbourNetArgs {
89    fn default() -> Self {
90        Self {
91            input: String::new(),
92            output_prefix: String::from("output"),
93            ordering: OrderingMethod::Multiway,
94            inference: InferenceMethod::ActiveSet,
95            nnls_params: NNLSParams::default(),
96        }
97    }
98}
fast_nnt/cli.rs

fast_nnt/
cli.rs
