hapsburg.figures.plot_ibdx
==========================

.. py:module:: hapsburg.figures.plot_ibdx

.. autoapi-nested-parse::

   Plotting function to plot IBD X Estimates from Ne
   @ Author: Harald Ringbauer, 2020, All rights reserved



Functions
---------

.. autoapisummary::

   hapsburg.figures.plot_ibdx.new_column
   hapsburg.figures.plot_ibdx.give_sub_df
   hapsburg.figures.plot_ibdx.give_stats_cm_bin
   hapsburg.figures.plot_ibdx.get_IBD_stats
   hapsburg.figures.plot_ibdx.get_IBD_stats_pops
   hapsburg.figures.plot_ibdx.get_ci_counts
   hapsburg.figures.plot_ibdx.create_Ne_roh_nr
   hapsburg.figures.plot_ibdx.create_ne_dct
   hapsburg.figures.plot_ibdx.plot_IBD_fracs
   hapsburg.figures.plot_ibdx.plot_ibd_curves
   hapsburg.figures.plot_ibdx.plot_ne_dct


Module Contents
---------------

.. py:function:: new_column(df, df_meta, col='New Clade', col_new='', match_col='iid')

   Maps Entries from meta dataframe onto the IBD dataframe.
   Return modified dataframe


.. py:function:: 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


.. py:function:: give_stats_cm_bin(df, cms=[4, 6, 8, 10, 12], col_b='n_roh>', output=True)

   print some stats about IBD dataframe df
   col_b: Start of IBD/ROH column. If one of cms 0 use open
   upper bound


.. py:function:: get_IBD_stats(df, pop1='', pop2='', col='clade', col_b='n_roh>', cms=[4, 6, 8, 10, 12], output=False, a=0.05)

   Get IBD fraction statistics.
   a: Signficance level
   Return fractions, confidence intervalls as well as
   number of pairsise comparisons
   col_b: Start of IBD/ROH column


.. py:function:: get_IBD_stats_pops(df, pops1=[], pops2=[], col='clade', col_b='n_roh>', cms=[4, 6, 8, 10, 12], output=False, a=0.05)

   Get IBD fraction statistics for list of pop pairs.
   Returns lists of fractions, confidence intervalls as well as
   number of pairsise comparisons.
   a: Significance Level
   col_b: Start of IBD/ROH column


.. py:function:: 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


.. py:function:: create_Ne_roh_nr(Ns=[400, 800, 1600, 3200, 6400], bins=[[0.04, 0.08], [0.08, 0.12], [0.12, 0.2], [0.2, 3.0]], bin_n=10000, chr_lgts=[])

   Create ROH sharing in list of bins (list of [begin,end])
   for panmictic population sizes
   Ns: List of population sizes
   bins: Length Bins (in Morgan) to calculate expectations from
   return sharing [len(degrees), len(bins)]


.. py:function:: create_ne_dct(cms, ns, chr_lgts=[1.8085 * 2 / 3])

   Create Dictionary of Ne


.. py:function:: plot_IBD_fracs(ax, cms1, fracs, cis=[], n=0, c='maroon', xlim=[4, 12], ylim=[0.001, 1.0], fs=(6, 6), lw=2, ms=6, yscale='log', show=False, hlines=[], lw_h=0.8, label='')

   Plot IBD fractions in cM bins. Plots on axis.
   ax: Axis to plot on.
   cms1: Vector of cM mean bins.
   fracs: Fractions to plot on y
   cis: Confidence Intervals [n elements of length 2]


.. py:function:: plot_ibd_curves(figsize=(6, 6), labels=[], cms=[], fracs=[], cis=[], fs_big=16, fs_l=12, fs_t=12, label_idx=-1, xlim=[4, 12], ylim=[0.005, 1.1], colors=[], ylabel='# IBD per pair of male X', xlabel='centimorgan [2 cM bins]', savepath='', dpi=400, leg_loc='upper right', hlines=[0.001, 0.01, 0.1, 1.0], offset=0, dct_ne={}, frameon=False)

   Plot IBD X curves on one plot.
   offset: Offset along x axis of each y value.
   label_idx: If>=0 Plot Ne labels on curves on according bin


.. py:function:: plot_ne_dct(ax, dct_ne={}, bins=[], label_idx=-1, c='lightgray', fs=12)

   Plot ne: ROH sharing onto axis