Esl Mixdchlet

esl-mixdchlet

Quick Start

• Command: esl-mixdchlet
• Local executable: /home/vimalinx/miniforge3/envs/bio/bin/esl-mixdchlet
• Version: 0.49
• Reference: See references/help.md for detailed options and usage

When To Use This Tool

• Use esl-mixdchlet when you need to train or inspect mixture Dirichlet priors from count vectors, such as emission or transition counts from profile-model training sets.
• Use fit to estimate a new prior, score to evaluate count data against an existing prior, gen to synthesize count vectors, and sample to create random priors for testing.
• It is most relevant in HMMER or Infernal-style modeling pipelines where count vectors are already available and you need reusable prior files.

Common Patterns

# Fit a 9-component amino-acid prior from count vectors of length 20
esl-mixdchlet fit 9 20 counts.tsv aa.mixdchlet

# Score observed counts against an existing prior
esl-mixdchlet score aa.mixdchlet counts.tsv

# Generate 500 synthetic count vectors with 50 counts each
esl-mixdchlet gen -N 500 -M 50 aa.mixdchlet > synthetic.counts

# Sample a random 4-component nucleotide prior for testing
esl-mixdchlet sample -K 4 -Q 4 > random.mixdchlet

Recommended Workflow

1. Decide which subcommand matches the task: fit, score, gen, or sample.
2. For fit, prepare one count vector per line with exactly K numeric fields; weighted real-valued counts are allowed.
3. Use -s <seed> whenever you need reproducible fitting, sampling, or data generation.
4. Validate the resulting prior by scoring held-out counts or by generating synthetic data for sanity checks.
5. Feed the saved mixture Dirichlet file into downstream model-building code only after the component count Q and alphabet size K look sensible.

Guardrails

• Top-level and subcommand help both use -h; --help is rejected locally.
• --version works at the top level, which is unusual for this Easel family.
• fit takes arguments as Q K in_countfile out_mixdchlet, but the written prior file begins with K Q on its first line; do not mix those orders up.
• sample writes the sampled prior to stdout unless redirected.
• score only evaluates data; it does not refit parameters.