`ancIBD.ibd_stats.funcs`

Module Contents

`new_columns`(df, df_meta[, col, col_new, match_col])	Maps Entries from meta dataframe onto the IBD dataframe.
`find_relatives`(df[, iid, min_ibd, cm])	Identify all relatives of a given sample
`rc_date`(age)	Ascertain whether age is rc.
`plot_age_diff`(df[, figsize, title, xlim, ylim, cs, ...])	Plot the Age Difference between two samples
`give_sub_df`(df[, pop1, pop2, col, output, exact])	Return sub dataframe where pair across pop1 and pop2
`remove_iids`(df[, iids])	Remove sample from pw. Indivdiual IID dataframe if pair contains one of flagged samples.
`give_stats_cm_bin`(df[, cms, binary, output])	Return counts of IBD in bins.
`get_IBD_stats`(df[, pop1, pop2, col, exact, cms, ...])	Get IBD fraction statistics.
`get_IBD_stats_pops`(df[, pops1, pops2, col, cms, ...])	Get IBD fraction statistics for list of pop pairs.
`get_ci_counts`(counts, n[, a, minc])	Get Confidence Intervalls from counts and
`ibd_stats_pop_pairs`(df, pop_pairs[, cms, col, a, ...])	For a list of population pairs, get ibd summary statistics for all pairs of
`create_ibd_pop_pair_df`(pop_pairs, ns, fracs, cis[, ...])	Save Population Pair dataframe if needed

ancIBD.ibd_stats.funcs.new_columns(df, df_meta, col='New Clade', col_new='', match_col='iid'): Maps Entries from meta dataframe onto the IBD dataframe. Return modified dataframe

ancIBD.ibd_stats.funcs.find_relatives(df, iid='', min_ibd=20, cm=20): Identify all relatives of a given sample

ancIBD.ibd_stats.funcs.rc_date(age): Ascertain whether age is rc. age: Can be Array

ancIBD.ibd_stats.funcs.plot_age_diff(df, figsize=(8, 8), title='', xlim=[-2000, 2000], ylim=[10, 5000], cs=['red', 'green'], fs=14, yscale='log', rcdate=False): Plot the Age Difference between two samples

ancIBD.ibd_stats.funcs.give_sub_df(df, pop1='La Caleta', pop2='La Caleta', col='clst', output=True, exact=False): Return sub dataframe where pair across pop1 and pop2

ancIBD.ibd_stats.funcs.remove_iids(df, iids=[]): Remove sample from pw. Indivdiual IID dataframe if pair contains one of flagged samples. Return Updated Dataframe. df: Input Dataframe iids: List of IIDs to remove

ancIBD.ibd_stats.funcs.give_stats_cm_bin(df, cms=[8, 12, 16, 20], binary=True, output=True): Return counts of IBD in bins. df: IBD dataframe df of pw. individuals cms: Which bins to look into binary: Only count existing (e.g. 1/0 values) If upper bound 0: Take infinite bin

ancIBD.ibd_stats.funcs.get_IBD_stats(df, pop1='', pop2='', col='clade', exact=False, cms=[4, 6, 8, 10, 12], binary=True, output=False, a=0.05): Get IBD fraction statistics. a: Signficance level binary: Only count existence of IBD, not total count [0/1 per pair] Return fractions, confidence intervalls as well as number of pairsise comparisons

ancIBD.ibd_stats.funcs.get_IBD_stats_pops(df, pops1=[], pops2=[], col='clade', cms=[4, 6, 8, 10, 12], output=False, binary=True, exact=False, a=0.05): Get IBD fraction statistics for list of pop pairs. Return lists of fractions, confidence intervalls as well as number of pairwise comparisons. a: Significance Level binary: Only count existence [0/1 possible per pair]

ancIBD.ibd_stats.funcs.get_ci_counts(counts, n, a=0.05, minc=0.0001): Get Confidence Intervalls from counts and trials. Return list of CIS (lentght 2 each) counts: Array of Counts n: Total number of trials a: Signficance level

ancIBD.ibd_stats.funcs.ibd_stats_pop_pairs(df, pop_pairs, cms=[8, 12, 16, 20, 0], col='label_region', a=0.32, binary=True, output=False, exact=True): For a list of population pairs, get ibd summary statistics for all pairs of individuals. Return list of ractions, list of CIs, list of #comparisons

ancIBD.ibd_stats.funcs.create_ibd_pop_pair_df(pop_pairs, ns, fracs, cis, cms=[8, 12, 16, 20, 0], savepath=''): Save Population Pair dataframe if needed