# Use the traffic.read_times() function to read in "times" dict.
# Times dict has keys 0..23 for hours, values are wait-times in seconds
# for traffic at that hour over a whole year.
import traffic
times = traffic.read_times('commute-times.txt')
times

{0: 922633,
 1: 870481,
 2: 814756,
 3: 850357,
 4: 895908,
 5: 923887,
 6: 951961,
 7: 1193951,
 8: 1142612,
 9: 944167,
 10: 914750,
 11: 868584,
 12: 882351,
 13: 862107,
 14: 897515,
 15: 905037,
 16: 927800,
 17: 986690,
 18: 918104,
 19: 900906,
 20: 883822,
 21: 845436,
 22: 844618,
 23: 902191}

# Use traffic.div_times() to scale it down to seconds for one day.
times = traffic.div_times(times, 365)
times

{0: 2527.7616438356163,
 1: 2384.8794520547945,
 2: 2232.2082191780823,
 3: 2329.745205479452,
 4: 2454.5424657534245,
 5: 2531.1972602739725,
 6: 2608.1123287671235,
 7: 3271.0986301369862,
 8: 3130.4438356164383,
 9: 2586.758904109589,
 10: 2506.1643835616437,
 11: 2379.682191780822,
 12: 2417.4,
 13: 2361.93698630137,
 14: 2458.945205479452,
 15: 2479.5534246575344,
 16: 2541.917808219178,
 17: 2703.2602739726026,
 18: 2515.353424657534,
 19: 2468.2356164383564,
 20: 2421.4301369863015,
 21: 2316.26301369863,
 22: 2314.0219178082193,
 23: 2471.7561643835616}

# Plotting works on *lists* - make a len-24 "waits" list
# from the times dict.

waits = [ times[key]  for key in range(24)   ]
waits

[2527.7616438356163,
 2384.8794520547945,
 2232.2082191780823,
 2329.745205479452,
 2454.5424657534245,
 2531.1972602739725,
 2608.1123287671235,
 3271.0986301369862,
 3130.4438356164383,
 2586.758904109589,
 2506.1643835616437,
 2379.682191780822,
 2417.4,
 2361.93698630137,
 2458.945205479452,
 2479.5534246575344,
 2541.917808219178,
 2703.2602739726026,
 2515.353424657534,
 2468.2356164383564,
 2421.4301369863015,
 2316.26301369863,
 2314.0219178082193,
 2471.7561643835616]

# This is the canonical line to import matplotlib using the name "plt"
import matplotlib.pyplot as plt

# 1. Simplest: plot a 1-d list of values, no x-values specified
plt.plot(waits)
plt.show()

# 2. More typical, provide [ x-values ] and [ y-values ] lists
# Here we narrow the data to the hours 6..20.
# (Comprehension is not strictly needed here, but a common pattern)
plt.plot([i for i in range(4, 22)], waits[4:22])
plt.show()

# 3. Like above, but set size, add labels, can play with line color and width
plt.figure(figsize=(10, 5))  # figxize() unit is like 0.5 inch
plt.title('Commute times')
plt.xlabel('Hour of day')
plt.ylabel('Commute time in seconds')
plt.plot([i for i in range(4,21)], waits[4:21], color='yellow', linewidth=29)
plt.show()