Streamline commandline args

src/data.hpp

@@ -347,7 +347,7 @@ class Data
 							n_signif_envs_sq++;
 						}
 					} catch (const std::exception &e) {
-						if (p.xtra_verbose) {
+						if (p.debug) {
 							std::cout << "Error when checking for significant squared envs ";
 							std::cout << "(I believe this is due to attempting linear regression ";
 							std::cout << "with a singular matrix)" << std::endl;

src/parameters.hpp

@@ -25,11 +25,11 @@ class parameters {
 	int env_update_repeats;
 	unsigned int n_thread, main_chunk_size, gxe_chunk_size;
 	std::uint32_t range_start, range_end;
-	bool range, maf_lim, info_lim, xtra_verbose, joint_covar_update, mode_RHEreg_LM;
+	bool range, maf_lim, info_lim, joint_covar_update, mode_RHEreg_LM;
 	bool mode_vb, use_vb_on_covars;
 	bool select_rsid, interaction_analysis, verbose, low_mem;
 	bool elbo_tol_set_by_user, alpha_tol_set_by_user, mode_empirical_bayes;
-	bool keep_constant_variants, user_requests_round1;
+	bool keep_constant_variants;
 	bool mode_alternating_updates, mode_RHE, mode_RHE_fast;
 	bool mode_no_gxe, debug, force_write_vparams;
 	bool init_weights_with_snpwise_scan, flip_high_maf_variants, min_spike_diff_set;
@@ -126,14 +126,12 @@ class parameters {
 		mode_dump_processed_data = false;
 		// check allele probs sum to 1 by default
 		verbose = false;
-		xtra_verbose = false;
 		use_vb_on_covars = true;
 		alpha_tol_set_by_user = false;
 		elbo_tol_set_by_user = false;
 		elbo_tol = 0.01;
 		alpha_tol = 0.001;
 		keep_constant_variants = false;
-		user_requests_round1 = false;
 	}
 
 	~parameters() = default;

src/parse_arguments.cpp

@@ -79,9 +79,9 @@ void parse_arguments(parameters &p, int argc, char **argv) {
 
 	options.add_options("General")
 	    ("verbose", "", cxxopts::value<bool>(p.verbose))
-	    ("bgen", "Path to BGEN file. This must be indexed (eg. with BGENIX). By default this is stored in RAM using O(NM) bytes.", cxxopts::value<std::string>(p.bgen_file))
-	    ("streamBgen", "Path to BGEN file. This must be indexed (eg. with BGENIX). This can be used with --RHEreg or --singleSnpStats.", cxxopts::value<std::string>())
-	    ("mStreamBgen", "Text file containing paths to multiple BGEN files. They must all be indexed (eg. with BGENIX). This can be used with --RHE or --mode_calc_snpstats.", cxxopts::value<std::string>())
+	    ("bgen", "Path to BGEN file, genetic data stored in RAM using O(NM) bytes.", cxxopts::value<std::string>(p.bgen_file))
+	    ("streamBgen", "Path to BGEN file, genetic data streamed from file. Used for single SNP association testing or randomized HE regression.", cxxopts::value<std::string>())
+	    ("mStreamBgen", "Text file containing paths to multiple BGEN files, genetic data streamed from file.", cxxopts::value<std::string>())
 	    ("pheno", "Path to phenotype file. Must have a header and have the same number of rows as samples in the BGEN file.", cxxopts::value<std::string>(p.pheno_file))
 	    ("covar", "Path to file of covariates. Must have a header and have the same number of rows as samples in the BGEN file.", cxxopts::value<std::string>(p.covar_file))
 	    ("environment", "Path to file of environmental variables. Must have a header and have the same number of rows as samples in the BGEN file.", cxxopts::value<std::string>(p.env_file))
@@ -112,8 +112,8 @@ void parse_arguments(parameters &p, int argc, char **argv) {
 	;
 
 	options.add_options("RHE")
-	    ("RHEreg", "Run randomised HE-regression algorithm", cxxopts::value<bool>())
-	    ("RHEreg-groups", "Text file containing group of each SNP for use with multicomponent randomised HE regression", cxxopts::value<std::string>())
+	    ("RHEreg", "Run randomized HE-regression algorithm", cxxopts::value<bool>())
+	    ("RHEreg-groups", "Text file containing group of each SNP for use with multicomponent randomized HE regression", cxxopts::value<std::string>())
 	    ("mRHEreg-groups", "Text file to paths of --RHE-groups files. Each should correspond to the bgen files given in --mStreamBgen", cxxopts::value<std::string>())
 	    ("n-RHEreg-samples", "Number of random vectors used in RHE algorithm (default 40)", cxxopts::value<long>(p.n_pve_samples))
 	    ("n-RHEreg-jacknife", "Number of jacknife samples used in RHE algorithm (default 100)", cxxopts::value<long>(p.n_jacknife))
@@ -333,16 +333,11 @@ void parse_arguments(parameters &p, int argc, char **argv) {
 		}
 		if(opts.count("xtra-verbose")) {
 			p.verbose = true;
-			p.xtra_verbose = true;
-			if(!opts.count("param_dump_interval")) {
-				p.param_dump_interval = 50;
-			}
 		}
 
 		if(opts.count("mode-debug")) {
 			p.debug = true;
 			p.verbose = true;
-			p.xtra_verbose = true;
 			if(!opts.count("param_dump_interval")) {
 				p.param_dump_interval = 50;
 			}

src/rhe_reg.cpp

@@ -343,7 +343,7 @@ void RHEreg::compute_RHE_trace_operators() {
 			}
 		}
 		if (p.verbose) std::cout << n_var << " variants pass QC filters" << std::endl;
-		if (p.xtra_verbose && p.RHE_multicomponent) {
+		if (p.debug && p.RHE_multicomponent) {
 			std::cout << n_find_operations << " find operations performed" << std::endl;
 		}
 	}
@@ -398,7 +398,7 @@ void RHEreg::solve_RHE(std::vector<RHEreg_Component>& components) {
 
 
 	// Write to file
-	if(world_rank == 0 && p.xtra_verbose) {
+	if(world_rank == 0 && p.debug) {
 		auto filename = fileUtils::fstream_init(outf, p.out_file, "", "_h2_dump");
 		std::cout << "Dumping Heritability to " << filename << std::endl;
 		Eigen::VectorXd tmp(Eigen::Map<Eigen::VectorXd>(CC.data(),CC.cols()*CC.rows()));

src/vbayes.hpp

@@ -345,7 +345,7 @@ class VBayes {
 	                  std::vector<Hyps>& all_hyps,
 	                  std::vector<VbTracker>& all_tracker){
 		// minimise KL Divergence and assign elbo estimate
-		if (p.xtra_verbose) std::cout << "Starting inner loop" << std::endl;
+		if (p.debug) std::cout << "Starting inner loop" << std::endl;
 		int print_interval = 25;
 		if(random_init) {
 			throw std::logic_error("Random start depreciated.");
@@ -502,7 +502,7 @@ class VBayes {
 		}
 
 		// Dump converged state
-		if (p.xtra_verbose) std::cout << "Dumping converged params" << std::endl;
+		if (p.debug) std::cout << "Dumping converged params" << std::endl;
 		for (int nn = 0; nn < n_grid; nn++) {
 			all_tracker[nn].dump_state("_converged", n_samples, n_covar, n_var,
 			                           n_env, n_effects,
@@ -630,7 +630,7 @@ class VBayes {
 		//
 		if(p.joint_covar_update) {
 			if(first_covar_update) {
-				if(p.xtra_verbose) {
+				if(p.debug) {
 					std::cout << "Performing first VB update of covar effects" << std::endl;
 				}
 				Eigen::MatrixXd CtCRidge = C.transpose() * C;