### Load data

dataloader = csv_dataloader()

dataloader.load('output/data_cache.pk')

print "Read in finished"

train_id = dataloader.id

train_data = dataloader.data_retrieve(train_id)

_, pos_id, neg_id = dataloader.balance(train_id, 'full')

train_data_pos = dataloader.data_retrieve(pos_id)

train_data_neg = dataloader.data_retrieve(neg_id)

tokens = sum(train_data['data'].viewvalues(), [])

tokens_pos = sum(train_data_pos['data'].viewvalues(), [])

tokens_neg = sum(train_data_neg['data'].viewvalues(), [])

### Calculate LIWC hist

LIWC = get_LIWC()

LIWC_hist = LIWC.encode(tokens, normalize=False)

LIWC_hist_pos = LIWC.encode(tokens_pos, normalize=True)

LIWC_hist_neg = LIWC.encode(tokens_neg, normalize=True)

fig = plt.figure()

ax = fig.add_subplot(2,1,1)

width = 0.3

bar0 = ax.bar(np.arange(67)+width, LIWC_hist, width)

#ax.set_xlabel('Category')

ax.set_ylabel('Frequency')

ax.set_title('(a)')

ax = fig.add_subplot(2,1,2)

bar1 = ax.bar(np.arange(67)+width, LIWC_hist_pos, width, color='r')

bar2 = ax.bar(np.arange(67)+2*width, LIWC_hist_neg, width, color='g')

labels = list(LIWC.category.viewvalues())

ax.set_label(['Postive', 'Negative'])

ax.set_xticks(np.arange(67)+2*width)

ax.set_xticklabels(labels, rotation='vertical')

ax.set_xlabel('Category')

ax.set_ylabel('Percentage')

ax.set_title('(b)')

ax.grid(True)