snpio.io.genepop_reader.GenePopReader

class snpio.io.genepop_reader.GenePopReader(filename, popmapfile=None, allele_encoding=None, force_popmap=False, exclude_pops=None, include_pops=None, plot_format='png', prefix='snpio', verbose=False, debug=False)

Reads GenePop-formatted files into a GenotypeData object.

Supports: - 2-digit and 3-digit allele codings - Mixed ploidy: haploid and diploid entries - Missing data encoded as 0000, 000000, or partial (e.g., 1000, 0010) - Flexible locus headers (comma-separated or newline-separated)

Example

>>> gp = GenePopReader("example.gen", popmapfile="pops.txt")
>>> gp.snp_data
array([...])

__init__(filename, popmapfile=None, allele_encoding=None, force_popmap=False, exclude_pops=None, include_pops=None, plot_format='png', prefix='snpio', verbose=False, debug=False)

Initialize the GenePopReader.

This class reads a GenePop file and extracts genotype data, populations, and sample information.

Parameters:

• filename (str) – Path to the GenePop file.

• popmapfile (Optional[str]) – Path to the population map file.

• allele_encoding (Optional[dict[str, str]]) – Mapping of allele codes to IUPAC symbols.

• force_popmap (bool) – Whether to enforce population mapping.

• exclude_pops (Optional[List[str]]) – List of populations to exclude.

• include_pops (Optional[List[str]]) – List of populations to include.

• plot_format (str) – Format for output plots (default: “png”).

• prefix (str) – Prefix for output files (default: “snpio”).

• verbose (bool) – Whether to enable verbose logging (default: False).

• debug (bool) – Whether to enable debug mode (default: False).

Methods

__init__(filename[, popmapfile, ...])	Initialize the GenePopReader.
bgzip_file(filepath)	BGZips a VCF file using pysam's BGZFile, preserving parent directories.
build_vcf_header()	Dynamically builds the VCF header using the stored header object.
calc_missing(df, *[, use_pops])	Compute missing-value statistics with proper locus + sample names.
copy()	Create a deep copy of the GenotypeData or subclass object.
encode_to_vcf_format(snp_data, ref_alleles, ...)	Vectorized encoding of IUPAC codes into VCF GT strings.
get_population_indices()	Create a mapping from population IDs to sample indices.
get_ref_alt_alleles(data)	Determine ref/alt alleles for each locus from IUPAC-encoded genotypes.
get_reverse_iupac_mapping()	Creates a reverse mapping from IUPAC codes -> allele tuples.
load_aln()	Load the GenePop file and parse genotype data.
missingness_reports([prefix, zoom, ...])	Generate missingness reports and plots.
read_popmap()	Read population map from file to map samples to populations.
refs_alts_from_snp_data(snp_matrix)	Determine REF/ALT per locus from a (samples x loci) IUPAC matrix.
replace_alleles(row, ref, alts)	Replace the alleles in the VCF row with the corresponding VCF genotype codes.
set_alignment(snp_data, samples, ...[, ...])	Set the alignment data and sample IDs after filtering.
subset_with_popmap(my_popmap, samples, force)	Subset popmap and samples based on population criteria.
tabix_index(filepath)	Creates a Tabix index for a bgzipped VCF file.
update_vcf_attributes(snp_data, ...)	Update VCF attributes after genotype data changes.
write_genepop(output_file[, genotype_data, ...])	Write the SNP data in GenePop format.
write_phylip(output_file[, genotype_data, ...])	Write the stored alignment as a PHYLIP file.
write_popmap(filename)	Write the population map to a file.
write_structure(output_file[, onerow, ...])	Write the stored alignment as a STRUCTURE file.
write_vcf(output_filename[, hdf5_file_path, ...])	Writes the GenotypeData object data to a VCF file in chunks.

Attributes

alt	Get list of alternate alleles of length num_snps.
biallelic_mask	[n_loci] True where locus appears biallelic (A/C/G/T plus heterozygotes of those two).
has_multiallelic	True if any locus shows >2 unambiguous nucleotides.
has_popmap	True if population information is present.
het_mask	[n_samples, n_loci] True if genotype is heterozygous (IUPAC ambiguity codes).
inputs	Get GenotypeData keyword arguments as a dictionary.
is_empty	True if there are zero samples or loci.
is_missing_locus	[n_loci] True if an entire locus is missing across all samples.
loci_indices	Boolean array for retained loci in filtered alignment.
locus_names	Concrete locus names, generating defaults if absent.
missing_mask	Boolean mask [n_samples, n_loci] where True indicates a missing genotype.
missing_rate	Overall missing proportion in the alignment.
nbytes	Approximate RAM footprint of snp_data (bytes).
num_inds	Number of individuals (samples) in dataset.
num_pops	Number of populations in the dataset.
num_snps	Number of snps (loci) in the dataset.
observed_iupac_per_locus	Observed IUPAC codes per locus (excluding missing).
output_dir	Root output directory for this dataset.
per_individual_het_rate	Heterozygote proportion per individual (ignores missing).
per_individual_missing	Missing proportion per sample as a pandas Series indexed by sample name.
per_locus_het_rate	Heterozygote proportion per locus (ignores missing).
per_locus_missing	Missing proportion per locus as a pandas Series; uses marker names if present.
plot_kwargs	Backwards compatibility; convert PlotConfig to the old dict shape.
plots_dir	Standardized location for plots (pre/post-filtering aware).
pop_sizes	Population -> sample count.
pop_to_indices	Population -> list of sample indices (built from current popmap_inverse).
popmap	Dictionary mapping sample IDs to population IDs.
popmap_inverse	Dictionary mapping population IDs to lists of sample IDs.
populations	List of populations in the dataset.
ref	Get list of reference alleles of length num_snps.
reports_dir	Standardized location for reports (pre/post-filtering aware).
sample_index_map	Map sample ID -> row index (useful for subsetting).
sample_indices	Boolean array for retained samples in filtered alignment.
samples	List of sample IDs in the dataset.
shape	Tuple of (n_samples, n_loci) for the SNP data.
snp_data	Get the genotypes as a 2D list of shape (n_samples, n_loci).
snpsdict	Dictionary with Sample IDs as keys and lists of genotypes as values.
valid_mask	Boolean mask [n_samples, n_loci] where True = non-missing genotype.