snpio.plotting.plotting.Plotting

class snpio.plotting.plotting.Plotting(genotype_data, show=False, plot_format='png', dpi=300, plot_fontsize=18, plot_title_fontsize=22, despine=True, verbose=False, debug=False)[source]

Class containing various methods for generating plots based on genotype data.

This class is initialized with a GenotypeData object containing necessary data. The class attributes are set based on the provided values, the GenotypeData object, or default values.

genotype_data

Initialized GenotypeData object containing necessary data.

Type:: GenotypeData

prefix

Prefix string for output directories and files.

Type:: str

output_dir

Output directory for saving plots.

Type:: Path

show

Whether to display the plots.

Type:: bool

plot_format

Format in which to save the plots.

Type:: str

dpi

Resolution of the saved plots.

Type:: int

plot_fontsize

Font size for the plot labels.

Type:: int

plot_title_fontsize

Font size for the plot titles.

Type:: int

despine

Whether to remove the top and right plot axis spines.

Type:: bool

verbose

Whether to enable verbose logging.

Type:: bool

debug

Whether to enable debug logging.

Type:: bool

logger

Logger object for logging messages.

Type:: logging.Logger

boolean_filter_methods

List of boolean filter methods.

Type:: list

missing_filter_methods

List of missing data filter methods.

Type:: list

maf_filter_methods

List of MAF filter methods.

Type:: list

mpl_params

Default Matplotlib parameters for the plots.

Type:: dict

plot_sankey_filtering_report(): Plot a Sankey diagram for the filtering report.

plot_pca(): Plot a PCA scatter plot with 2 or 3 dimensions, colored by missing data proportions, and labeled by population with symbols for each sample.

plot_summary_statistics()[source]: Plot summary statistics per sample and per population on the same figure. The summary statistics are plotted as lines for each statistic (Ho, He, Pi, Fst).

plot_dapc(): Plot a DAPC scatter plot. with 2 or 3 dimensions, colored by population, and labeled by population with symbols for each sample.

plot_fst_heatmap(): Plot a heatmap of Fst values between populations, sorted by highest Fst and displaying only the lower triangle.

plot_fst_outliers()[source]: Plot a heatmap of Fst values for outlier SNPs, highlighting contributing population pairs.

plot_d_statistics()[source]: Create plots for D-statistics with multiple test corrections.

_set_logger(): Set the logger object based on the debug attribute. If debug is True, the logger will log debug messages.

_get_attribute_value()[source]: Determine the value for an attribute based on the provided argument, genotype_data attribute, or default value. If a value is provided during initialization, it is used. Otherwise, the genotype_data attribute is used if available. If neither is available, the default value is used.

_plot_summary_statistics_per_sample()[source]: Plot summary statistics per sample. If an axis is provided, the plot is drawn on that axis.

_plot_summary_statistics_per_population()[source]: Plot summary statistics per population. If an axis is provided, the plot is drawn on that axis.

__init__(genotype_data, show=False, plot_format='png', dpi=300, plot_fontsize=18, plot_title_fontsize=22, despine=True, verbose=False, debug=False)[source]

Initialize the Plotting class.

This class contains various methods for generating plots based on genotype data. The class is initialized with a GenotypeData object containing necessary data. The class attributes are set based on the provided values, the GenotypeData object, or default values.

Parameters:

genotype_data (GenotypeData) – Initialized GenotypeData object containing necessary data.
show (bool) – Whether to display the plots. Defaults to genotype_data.show if available, otherwise False.
plot_format (str) – The format in which to save the plots (e.g., ‘png’, ‘svg’). Defaults to genotype_data.plot_format if available, otherwise ‘png’.
dpi (int) – The resolution of the saved plots. Unused for vector plot_format types. Defaults to genotype_data.dpi if available, otherwise 300.
plot_fontsize (int) – The font size for the plot labels. Defaults to genotype_data.plot_fontsize if available, otherwise 18.
plot_title_fontsize (int) – The font size for the plot titles. Defaults to genotype_data.plot_title_fontsize if available, otherwise 22.
despine (bool) – Whether to remove the top and right plot axis spines. Defaults to genotype_data.despine if available, otherwise True.
verbose (bool) – Whether to enable verbose logging. Defaults to genotype_data.verbose if available, otherwise False.
debug (bool) – Whether to enable debug logging. Defaults to genotype_data.debug if available, otherwise False.

Note

The show, plot_format, dpi, plot_fontsize, plot_title_fontsize, despine, verbose, and debug attributes are set based on the provided values, the genotype_data object, or default values.
The output_dir attribute is set to the prefix_output/nremover/plots directory or the prefix_output/plots directory if the genotype data was not filtered when initializing the Plotting class.
The mpl_params dictionary contains default Matplotlib parameters for the plots and are updated with the mpl_params dictionary.
The plotting object is used to set the attributes based on the provided values, the genotype_data object, or default values.

Methods

`__init__`(genotype_data[, show, plot_format, ...])	Initialize the Plotting class.
`plot_allele_summary`(summary[, figsize])	Plot allele summary statistics from summarize_alleles output.
`plot_d_statistics`(df, method)	Create and save D-statistics plots and MultiQC reports.
`plot_d_statistics_heatmap`(df[, method_name])	Plots a heatmap of D-statistics colored by -log10(P-value).
`plot_dist_matrix`(df, *[, pvals, palette, ...])	Plot distance matrix.
`plot_dstat_chi_square_distribution`(df, ...)	Plots the distribution of Chi-square values for D-statistics.
`plot_dstat_pvalue_distribution`(df, method_name)	Plots the distribution of -log10(P-values) for D-statistics.
`plot_dstat_significance_counts`(df, method_name)	Plots the number of significant results per D-statistic.
`plot_fst_outliers`(outlier_snps, method[, ...])	Create a heatmap of Fst values for outlier SNPs, highlighting contributing population pairs.
`plot_gt_distribution`(df[, annotation_size])	Plot the distribution of genotype counts.
`plot_permutation_dist`(obs_fst, dist, ...[, ...])	Plot the permutation distribution of Fst values.
`plot_pop_counts`(populations)	Plot the population counts.
`plot_search_results`(df_combined)	Plot and save the filtering results based on the available data.
`plot_stacked_significance_barplot`(df, ...)	Creates a stacked bar plot of significance categories.
`plot_summary_statistics`(summary_statistics)	Plot summary statistics per sample and per population.
`visualize_missingness`(df[, prefix, zoom, ...])	Visualize missing data across loci, individuals, and populations.