hapsburg.figures.plot_bars
==========================

.. py:module:: hapsburg.figures.plot_bars

.. autoapi-nested-parse::

   Main Inference Class for HMM. Wrapper for Inerence of Posterior.
   @ Author: Harald Ringbauer, 2019, All rights reserved



Functions
---------

.. autoapisummary::

   hapsburg.figures.plot_bars.create_cousins_roh
   hapsburg.figures.plot_bars.create_Ne_roh
   hapsburg.figures.plot_bars.std_Ne_roh
   hapsburg.figures.plot_bars.plot_bar_ax
   hapsburg.figures.plot_bars.plot_close_kin
   hapsburg.figures.plot_bars.plot_panel_row
   hapsburg.figures.plot_bars.plot_legend_only
   hapsburg.figures.plot_bars.prepare_dfs_plot
   hapsburg.figures.plot_bars.prep_dfs_plot_exact
   hapsburg.figures.plot_bars.prep_xlabels


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

.. py:function:: create_cousins_roh(degrees=[1, 2, 3], bins=[[0.04, 0.08], [0.08, 0.12], [0.12, 0.2], [0.2, 3.0]], bin_n=10000)

   Create ROH sharing in list of bins (list of [begin,end])
   for Cousins of degree degrees [list]
   return sharing [len(degrees), len(bins)]


.. py:function:: create_Ne_roh(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)

   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:: std_Ne_roh(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)

   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:: plot_bar_ax(ax, y, bins=[], c=['#313695', '#abd9e9', '#fee090', '#d7191c'], x_ticks=[], ec='silver', fs_l=10, fs_y=10, fs_x=8, fs_t=10, alpha=1.0, barWidth=0.95, ylim=[0, 220], stds=[], title='', ha_title='left', bold_title=False, yticks=False, ylabel='Sum Inferred ROH>4cM [cM]', legend=False, r_title=0, hlines=[])

   Plot bars of ROH on Axis.
   ax: Where to Plot on
   y: Array of ROH to plot: [n Inds, k Legnth Bins]
   c: Which colors to plot
   bins: List of Bins (needed for legend - plotted if len()>0)
   yticks: Whether to plot Y tick Labels
   legend: Whether to plot Legend
   fs_x, fs_y: Fontsize on the x and yLabels
   r_title: Rotation of the title
   hlines: List where to plot hlines


.. py:function:: plot_close_kin(ax, y, y_plot=100, cutoffs=[50, 100], c='r', ec='k', lw=1, ss=[10, 14], m_cs=['v', 's'], bin_idx=-1)

   Plot Symbols for close kin at height y_symbol
   ss: Sizes of markers [List]
   m_cs: marker_colors [List]
   cutoffs: cutoffs [List]


.. py:function:: plot_panel_row(plot_dfs, wspace=0.05, hspace=0.01, figsize=(24, 3.5), savepath='', x_labels=[], ylabel='Sum Inferred ROH>4cM [cM]', c=['#313695', '#abd9e9', '#fee090', '#d7191c'], ylim=[0, 250], r_title=90, bolds=[], fs_l=10, fs_y=10, fs_x=8, fs_t=10, ha_title='left', hspace_leg=1, leg_pos=-2, show=True, title_col='clst', titles=[], hlines=[], cols=['sum_roh>4', 'sum_roh>8', 'sum_roh>12', 'sum_roh>20'], bins=[[0.04, 0.08], [0.08, 0.12], [0.12, 0.2], [0.2, 3.0]], degrees=[1, 2, 3], Ns=[400, 800, 1600, 3200, 6400], ticks_c=['1st C.', '2nd C.', '3rd C.'], cutoffs=[], ec='k', lw=1, ss=[40, 50], m_cs=['v', 's'], sym_ofst=-10, bin_idx=-1)

   Plot row of ROH bin plots from plot_dfs (each df one panel)
   leg_pos = Where to plot legend (if outside range no legend plot)
   r_title: How much to rotate the title
   bolds: which titles to bold [list of bools]
   ha_title: Horizontal alignment of the titles
   fs_l, fs_y, fs_x, fs_t: Fontsize of legend, y and x labels and titles
   hspace_leg: Horizontol space between data plots and legend
   gs: Gridspec: If given plot on there
   legends: Whether to plot the two legends
   titles: If given, list of titles
   title_col: If not title use this column of the dataframe
   hlines: Where to plot horizontal lines
   cols: List of Column Names for plot_dfs (assumes > in increasing order)
   bins: list of length bins to plot [[a1,a2],...[z1,z2]]
   Ns: What population sizes to plot in barplot [list]
   degrees: What degrees of Cousins to plot. [list]
   ticks_c: Tick Labels for Cousin Legend [list]
   x_labels: If FALSE, don't plot any xlabels, if empty list default [list]


.. py:function:: plot_legend_only(figsize=(7, 6), wspace=0.05, hspace=0.01, savepath='', hlines=[], fs_l=10, fs_y=10, fs_x=8, fs_t=10, c=['#313695', '#abd9e9', '#fee090', '#d7191c'], bins=[[0.04, 0.08], [0.08, 0.12], [0.12, 0.2], [0.2, 3.0]], ha_title='center', degrees=[1, 2, 3], Ns=[400, 800, 1600, 3200, 6400], ylim=[0, 250], x_ticks_c=['1st C.', '2nd C.', '3rd C.'], title_c='Recent Loops', title_N='Small Pop. Size', y_label='Expected Sum ROH>4cM [cM]')

   Plot Inbreeding from recent Cousins as well as small pop size.
   bins: list of length bins to plot [[a1,a2],...[z1,z2]]
   Ns: What population sizes to plot in barplot [list]
   degrees: What degrees of Cousins to plot. [list]


.. py:function:: prepare_dfs_plot(df, cms=[4, 8, 12], col_group='clst', split_modern=True, mod_group='pop', sortcol=0)

   Prepare the Dataframe which to plot
   Return split up (and sorted) list of df, return list of column names
   df: Master Dataframe
   cms: Minimum Length of Chromosomes in bins
   sortcol: By which > column to sort within group [int]
   if split_modern, split age==0 samples by mod_group


.. py:function:: prep_dfs_plot_exact(df, pops=[], col_group='pop', mod_only=False, exact=True, cm_sort=4)

   Pull all matching populations and return list of dataframes


.. py:function:: prep_xlabels(plot_dfs, col_age='age')

   Prepare vector of x labels
   corresponding to ages of individuals