fast-nnt 0.2.5

Rust implementation of the SplitsTree NeighborNet algorithm.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95

use clap::{Args, Parser, Subcommand};

use crate::{
    ordering::OrderingMethod,
    weights::{InferenceMethod, active_set_weights::NNLSParams},
};

#[derive(Parser, Debug)]
#[command(version, about, long_about = None)]
pub struct ProgramArgs {
    #[command(subcommand)]
    pub subcommand: ProgramSubcommand,
    #[arg(
        short,
        long,
        default_value = "1",
        global = true,
        help = "Number of threads to use."
    )]
    pub threads: usize,
    #[arg(
        short,
        long,
        default_value = "false",
        conflicts_with = "quiet",
        global = true
    )]
    pub verbose: bool,
    #[arg(
        short,
        long,
        default_value = "false",
        conflicts_with = "verbose",
        global = true
    )]
    pub quiet: bool,
    #[arg(
        short = 'd',
        long,
        default_value = "output",
        global = true,
        help = "Output directory"
    )]
    pub output_directory: String,
}

#[derive(Subcommand, Debug)]
pub enum ProgramSubcommand {
    #[clap(
        name = "neighbour_net",
        about = "Run the SplitsTree NeighborNet algorithm"
    )]
    NeighborNet(NeighbourNetArgs),
}

#[derive(Args, Debug)]
pub struct NeighbourNetArgs {
    /// Input distance matrix file path
    #[arg(short, long, help = "Input distance matrix file path", required = true)]
    pub input: String,
    #[arg(
        short,
        long,
        help = "Output prefix for result files",
        default_value = "output"
    )]
    pub output_prefix: String,
    #[arg(
        short = 'O',
        long,
        help = "The ordering algorithm to use to get splits cycle",
        default_value = "multiway"
    )]
    pub ordering: OrderingMethod,
    #[arg(
        long,
        help = "Split-weight inference algorithm",
        default_value = "active-set"
    )]
    pub inference: InferenceMethod,
    #[clap(flatten)]
    pub nnls_params: NNLSParams,
}

impl Default for NeighbourNetArgs {
    fn default() -> Self {
        Self {
            input: String::new(),
            output_prefix: String::from("output"),
            ordering: OrderingMethod::Multiway,
            inference: InferenceMethod::ActiveSet,
            nnls_params: NNLSParams::default(),
        }
    }
}