pyiwe 0.0.8.post3

Python wrapper for TNT (Tree analysis using New Technology) implied weighting with clades support

Installation

1. Pip:

$ pip install pyiwe

2. conda:

$ conda install -c anaconda -c conda-forge -c alexander-pv pyiwe

3. From source:

$ git clone [e-mail]:alexander-pv/pyiwe.git && cd pyiwe
$ pip install .

Terminal TNT will be installed automatically.

The package file from PyPi or conda does not include terminal TNT. To install it, open python in terminal mode and import pyiwe package.

$ python
$ >>> import pyiwe

Tutorial

• implied_weighting_theory.ipynb, theory behind implied weighting with fitting functions plots to play;

• pyiwe_example.ipynb, examples of reading TNT trees, plotting trees, getting branch supports and concavity values distributions for each clade in a tree based on TNT feature matrices;

• pyiwe_runner.py, terminal-based example for a quick start;

Run pyiwe_runner.py to see arguments help:

$ cd ./pyiwe/tutorial && python pyiwe_runner.py -h

Argument parser for pyiwe_runner.py

positional arguments:
  feat_matrix           str, path to the feature matrix for TNT

optional arguments:
  -h, --help            show this help message and exit
  -k_start k_start      float, minimum value in a linear scale or a degree in a logarithmic scale, default=1e-2
  -k_stop k_stop        float, maximum value in a linear scale or a degree in a logarithmic scale, default=1.5
  -k_num k_num          int, number of samples to generate, default=100
  -k_scale k_scale      str, scale of concavity values, `log` or `linear`, default=`log`
  -n_runs n_runs        int, the number of repeated IW runs, default=3
  -cutoff cutoff        float, cutoff value between 0.0 and 1.0 for a final majority rule tree, default=0.5
  -xmult_hits xmult_hits
                        int, produce N hits to the best length and stop, default=5
  -xmult_level xmult_level
                        int, set level of search (0-10). Use 0-2 for easy data, default=3
  -xmult_drift xmult_drift
                        int, cycles of drifting;, default=5
  -hold hold            int, a tree buffer to keep up to specified number of trees, default=500
  -output_folder output_folder
                        str, path to store data, default=./output
  -log_base log_base    float, base for calculating a log space for concavity constants, default=10.0
  -float_prec float_prec
                        int, Floating point calculations precision, default=5
  -tnt_seed tnt_seed    str, random seed properties for TNT, default=`1`
  -seed seed            str, random seed for Python numpy, default=42
  -tnt_echo tnt_echo    str, `=`, echo each command, `-`, don`t echo, default=`-`
  -memory memory        float, Memory to be used by macro language, in KB, default=10240
  -c                    bool, clear temp *.tre files in output folder after processing
  -v                    bool, add processing verbosity

Basic example:

$ cd ./pyiwe/tutorial
$ python pyiwe_runner.py ../pyiwe/tests/testdata/bryocorini/SI_4_Bryocorinae_matrix.tnt -c

References

• TNT source: http://www.lillo.org.ar/phylogeny/tnt (Goloboff, Farris, & Nixon, 2003)

• Biopython: https://biopython.org

• ETE, Python Environment for Tree Exploration: http://etetoolkit.org