import os
import pandas as pd
import sys
from datetime import datetime
from type import get_type
import matplotlib.pyplot as plt

# Get page views dataframe with type
page_views_df = get_type()

# Split page views datafame into logged in vs. logged out
logged_in = page_views_df[page_views_df['logged_in'] == True]
logged_out = page_views_df[page_views_df['logged_in'] == False]

# Count how many page views per type
logged_in_breakdown = pd.DataFrame(logged_in['type'].value_counts(dropna=False))
logged_out_breakdown = pd.DataFrame(logged_out['type'].value_counts(dropna=False))


# Plot pie chart for Logged In users
# Hardcode colors to match Fig 2.
labels_in = logged_in_breakdown.index.values
sizes_in = logged_in_breakdown['type']
explode_in = (0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.2)
colors_in = ('coral', 'orange', 'gold', 'orangered',
		'royalblue', 'cyan', 'skyblue' ,'purple' ,'turquoise', 'violet' )

# Plot
plt.pie(sizes_in, explode=explode_in, labels=labels_in,
        autopct='%1.1f%%', shadow=False, colors= colors_in)
plt.title('Fig 1. Logged In Breakdown')
plt.show()

# Plot pie chart for Logged Out users
# Hardcode colors to match Fig 1.
labels_out = logged_out_breakdown.index.values
sizes_out = logged_out_breakdown['type']
explode_out = (0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.2)
colors_out = ('orange', 'orangered', 'coral', 'gold',
		'royalblue', 'cyan', 'turquoise', 'purple', 'skyblue', 'violet' )


# Plot
plt.pie(sizes_out, explode=explode_out, labels=labels_out,
        autopct='%1.1f%%', shadow=False, colors= colors_out)
plt.title('Fig 2. Logged Out Breakdown')
plt.show()