import gzip
import os
import sys
from collections import Counter
from Ordinal.utils import jaggedness
import pickle
import numpy as np

amazon = np.genfromtxt('prevalence_votes1_reviews100.csv', delimiter='\t')
telescope = np.genfromtxt('fact_real_prevalences.csv', delimiter=',')[1:]

nclasses_amazon = amazon.shape[1]
nclasses_telescope = telescope.shape[1]

jags_amazon = np.asarray([jaggedness(p) for p in amazon])
jags_telescope = np.asarray([jaggedness(p) for p in telescope])

import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import seaborn as sns

sns.set_theme('paper')
sns.set_style('dark')
sns.set(font_scale=0.7)

# figure, axis = plt.subplots(1, 2, figsize=(8, 7))
ymax = 0.75

figure(figsize=(8, 4), dpi=300)

ax=plt.subplot(1, 2, 1)
classes = np.arange(1, nclasses_amazon+1)
plt.bar(classes, np.mean(amazon, axis=0), yerr=np.std(amazon, axis=0), width=1)
ax.set_ylim(0, ymax)
ax.set_xlabel("stars")
ax.set_xticks(classes)
ax.set_title(f'Amazon Books ({jags_amazon.mean():.4f})')

ax=plt.subplot(1, 2, 2)
# ax=plt.subplot(1, 1, 1)
classes = np.arange(1, nclasses_telescope+1)
plt.bar(classes, np.mean(telescope, axis=0), yerr=np.std(telescope, axis=0), width=1)
ax.set_ylim(0, ymax)
ax.set_xlabel("energy bin")
ax.set_xticks(classes)
ax.set_title(f'FACT Samples ({jags_telescope.mean():.4f})')


plt.subplots_adjust(wspace=0.1, hspace=0)
plt.savefig('prevalence_averages.pdf', bbox_inches='tight')