use adjustp::Procedure;
use colored::Colorize;
use hashbrown::HashSet;
use std::fmt::Debug;
use crate::{aggregation::GeneAggregation, io::HitList, model::ModelChoice, norm::Normalization};
#[derive(Default)]
pub struct Logger {
verbose: bool,
}
impl Logger {
pub fn new() -> Self {
Self { verbose: true }
}
pub fn new_silent() -> Self {
Self { verbose: false }
}
fn write_to_stderr<V: Debug>(prompt: &str, value: V) {
eprintln!(
"{:width$} {}",
format!(">> {}", prompt.bright_green()),
format!("{value:.5?}").white().bold(),
width = 30
);
}
pub fn start_mageck(&self) {
if self.verbose {
eprintln!("\n{}", "Run Configuration".bold().underline());
}
}
pub fn num_sgrnas(&self, x: &[String]) {
if self.verbose {
Self::write_to_stderr("Number of sgRNAs : ", x.len());
}
}
pub fn group_names(&self, controls: &[String], treatments: &[String]) {
if self.verbose {
Self::write_to_stderr("Control Group : ", controls);
Self::write_to_stderr("Treatment Group : ", treatments);
}
}
pub fn num_genes(&self, x: &[String]) {
if self.verbose {
let unique_genes = x.iter().cloned().collect::<HashSet<String>>();
Self::write_to_stderr("Number of Genes : ", unique_genes.len());
}
}
pub fn norm_method(&self, n: &Normalization) {
if self.verbose {
Self::write_to_stderr("Normalization Method : ", n);
}
}
pub fn aggregation_method(&self, g: &GeneAggregation) {
if self.verbose {
Self::write_to_stderr("Aggregation Method : ", g);
}
}
pub fn correction(&self, correction: Procedure) {
if self.verbose {
Self::write_to_stderr("P-Value Correction Method : ", correction);
}
}
pub fn filtering(&self, minimum: f64) {
if self.verbose {
eprintln!("\n{}", "Filtering Low Count sgRNAs".bold().underline());
Self::write_to_stderr("Minimum Base Mean : ", minimum);
}
}
pub fn num_filtered(&self, num_filtered: usize) {
if self.verbose {
Self::write_to_stderr("Number of Filtered sgRNAs : ", num_filtered);
}
}
pub fn start_mean_variance(&self) {
if self.verbose {
eprintln!("\n{}", "Modeling Mean Variance".bold().underline());
}
}
pub fn num_outliers(&self, x: usize) {
if self.verbose {
Self::write_to_stderr("Removed Outlier sgRNAs : ", x);
}
}
pub fn num_zeros(&self, x: usize) {
if self.verbose {
Self::write_to_stderr("Removed Zero sgRNAs : ", x);
}
}
pub fn num_varied(&self, x: usize) {
if self.verbose {
Self::write_to_stderr("Removed Undervaried sgRNAs : ", x);
}
}
pub fn ols_parameters(&self, model_choice: &ModelChoice, kappa: f64, beta: f64) {
if self.verbose {
Self::write_to_stderr("Linear Model Type : ", model_choice);
Self::write_to_stderr("Fit Parameter; K : ", kappa);
Self::write_to_stderr("Fit Parameter; B : ", beta);
}
}
pub fn start_gene_aggregation(&self) {
if self.verbose {
eprintln!("\n{}", "Performing Gene Aggregation".bold().underline());
}
}
pub fn report_rra_alpha(&self, alpha_low: f64, alpha_high: f64) {
if self.verbose {
Self::write_to_stderr("Alpha threshold low : ", alpha_low);
Self::write_to_stderr("Alpha threshold high : ", alpha_high);
}
}
pub fn permutation_sizes(&self, sizes: &[usize]) {
if self.verbose {
let mut sorted_sizes = sizes.to_owned();
sorted_sizes.sort_unstable();
Self::write_to_stderr("Permutation Sizes : ", sorted_sizes);
}
}
pub fn report_inc_params(
&self,
ntc_token: &str,
n_genes: usize,
fdr: f64,
group_size: usize,
n_draws: usize,
) {
if self.verbose {
Self::write_to_stderr("NTC Token : ", ntc_token);
Self::write_to_stderr("Number of Pseudogenes : ", n_genes);
Self::write_to_stderr("FDR : ", fdr);
Self::write_to_stderr("Group Size : ", group_size);
Self::write_to_stderr("Number of Draws : ", n_draws);
}
}
pub fn report_inc_low_threshold(&self, threshold: f64, use_product: bool) {
if self.verbose {
if use_product {
Self::write_to_stderr("Low Product Threshold : ", threshold);
} else {
Self::write_to_stderr("Low Pvalue Threshold : ", threshold);
}
}
}
pub fn report_inc_high_threshold(&self, threshold: f64, use_product: bool) {
if self.verbose {
if use_product {
Self::write_to_stderr("High Product Threshold : ", threshold);
} else {
Self::write_to_stderr("High Pvalue Threshold : ", threshold);
}
}
}
pub fn report_inc_ntc_std(&self, ntc_std: f64) {
if self.verbose {
Self::write_to_stderr("NTC Log2FC Std. Dev. : ", ntc_std);
}
}
pub fn convert_normalization(&self) {
if self.verbose {
eprintln!(
"\n{}: {}",
"Warning".bold().yellow(),
"Numeric instability found in median-ratio normalization. Performing total normalization instead.".bold()
);
}
}
pub fn hit_list(&self, hit_list: &HitList) {
if self.verbose {
eprintln!("\n{}", "Hits".bold().underline());
Self::write_to_stderr("Number of Hits : ", hit_list.num_total());
Self::write_to_stderr("Number Upregulated Hits : ", hit_list.num_enrichments());
Self::write_to_stderr("Number Downregulated Hits : ", hit_list.num_depletions());
}
}
}
#[cfg(test)]
mod testing {
use super::Logger;
use crate::aggregation::GeneAggregation;
use crate::model::ModelChoice;
use crate::norm::Normalization;
use adjustp::Procedure;
#[test]
fn test_logger() {
let logger = Logger::new();
logger.start_mageck();
logger.num_sgrnas(&["a".to_string(), "b".to_string()]);
logger.num_genes(&["a".to_string(), "b".to_string()]);
logger.norm_method(&Normalization::MedianRatio);
logger.aggregation_method(&GeneAggregation::Inc {
token: "ntc",
fdr: 0.05,
group_size: 5,
use_product: true,
n_draws: 100,
});
logger.correction(Procedure::Bonferroni);
logger.filtering(10.0);
logger.num_filtered(1);
logger.start_mean_variance();
logger.num_outliers(1);
logger.num_zeros(1);
logger.num_varied(1);
logger.ols_parameters(&ModelChoice::Ols, 1.0, 1.0);
logger.start_gene_aggregation();
logger.report_rra_alpha(1.0, 1.0);
logger.permutation_sizes(&[1, 2, 3]);
logger.report_inc_params("NTC", 1, 1.0, 1, 100);
logger.report_inc_low_threshold(1.0, false);
logger.report_inc_high_threshold(1.0, false);
logger.report_inc_low_threshold(1.0, true);
logger.report_inc_high_threshold(1.0, true);
logger.convert_normalization();
}
#[test]
fn test_logger_quiet() {
let logger = Logger::new_silent();
logger.start_mageck();
logger.num_sgrnas(&["a".to_string(), "b".to_string()]);
logger.num_genes(&["a".to_string(), "b".to_string()]);
logger.norm_method(&Normalization::MedianRatio);
logger.aggregation_method(&GeneAggregation::Inc {
token: "ntc",
fdr: 0.05,
group_size: 5,
use_product: false,
n_draws: 100,
});
logger.correction(Procedure::Bonferroni);
logger.filtering(10.0);
logger.num_filtered(1);
logger.start_mean_variance();
logger.num_outliers(1);
logger.num_zeros(1);
logger.num_varied(1);
logger.ols_parameters(&ModelChoice::Ols, 1.0, 1.0);
logger.start_gene_aggregation();
logger.report_rra_alpha(1.0, 1.0);
logger.permutation_sizes(&[1, 2, 3]);
logger.report_inc_params("NTC", 1, 1.0, 1, 100);
logger.report_inc_low_threshold(1.0, false);
logger.report_inc_high_threshold(1.0, false);
logger.report_inc_low_threshold(1.0, true);
logger.report_inc_high_threshold(1.0, true);
logger.convert_normalization();
}
}