plot_post.py

from __future__ import division
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from hpd import hpd


def plot_post(param_sample_vec, cred_mass=0.95, comp_val=False,
              ROPE=False, ylab='', xlab='parameter', fontsize=14, labelsize=14,
              title='', framealpha=1, facecolor='skyblue', edgecolor='white',
              show_mode=True, bins=50):

    #compute HDI
    HDI = hpd(param_sample_vec, 1-cred_mass)

    post_summary = {'mean':0,'median':0,'mode':0, 'hdi_mass':0,'hdi_low':0,
                   'hdi_high':0, 'comp_val':0, 'pc_gt_comp_val':0, 'ROPE_low':0,
                   'ROPE_high':0, 'pc_in_ROPE':0}
    post_summary['mean'] = np.mean(param_sample_vec)
    post_summary['median'] = np.median(param_sample_vec)
    post_summary['mode'] = stats.mode(param_sample_vec)[0]
    post_summary['hdi_mass'] = cred_mass
    post_summary['hdi_low'] = HDI[0]
    post_summary['hdi_high'] = HDI[1]

    # Plot histogram.
    n, bins, patches = plt.hist(param_sample_vec, normed=True, bins=bins,
                                edgecolor=edgecolor, facecolor=facecolor)
    plt.xlabel(xlab, fontsize=fontsize)
    plt.ylabel(ylab, fontsize=fontsize)
    plt.title(title, fontsize=fontsize)

    cv_ht = 0.75*np.max(n)
    cen_tend_ht = 0.9 * cv_ht
    ROPE_text_ht = 0.55 * cv_ht
#    # Display mean or mode:
    if show_mode:
        plt.plot(0, label='mode = %.2f' % post_summary['mode'], alpha=0)
    else:
        plt.plot(0, label='mean = %.2f' % post_summary['mean'], alpha=0)
    # Display the comparison value.

    if comp_val is not False:
        pc_gt_comp_val = 100 * np.sum(param_sample_vec > comp_val)/len(param_sample_vec)
        pc_lt_comp_val = 100 - pc_gt_comp_val
        plt.plot([comp_val, comp_val], [0, cv_ht], color='darkgreen',
                 linestyle='--', linewidth=2,
                 label='%.1f%% <%.1f < %.1f%%'
                 % (pc_lt_comp_val, comp_val, pc_gt_comp_val))
        post_summary['comp_val'] = comp_val
        post_summary['pc_gt_comp_val'] = pc_gt_comp_val
#    # Display the ROPE.
    if ROPE is not False:
        rope_col = 'darkred'
        pc_in_ROPE = round(np.sum((param_sample_vec > ROPE[0]) & (param_sample_vec < ROPE[1]))/len(param_sample_vec)*100)
        plt.plot([ROPE[0], ROPE[0]], [0, 0.96*ROPE_text_ht], color=rope_col,
                linestyle=':', linewidth=4,
                label='%.1f%% in ROPE' % pc_in_ROPE)
        plt.plot([ROPE[1], ROPE[1]], [0, 0.96*ROPE_text_ht], color=rope_col,
                linestyle=':', linewidth=4)
        post_summary['ROPE_low'] = ROPE[0] 
        post_summary['ROPE_high'] = ROPE[1] 
        post_summary['pc_in_ROPE'] = pc_in_ROPE
#    # Display the HDI.
    plt.plot(HDI, [0, 0], linewidth=6, color='k', label='HDI %.1f%% %.3f-%.3f' % (cred_mass*100, HDI[0], HDI[1]))
    plt.legend(loc='upper left', fontsize=labelsize, framealpha=framealpha)
    frame = plt.gca()
    frame.axes.get_yaxis().set_ticks([])
    return post_summary