Unique Image Timeline Visualization.py

%matplotlib inline
import matplotlib.pyplot as plt
from datetime import timedelta
from bson import ObjectId
import seaborn as sns
sns.set_style('whitegrid')
from itertools import cycle
cyclic_palette = cycle(sns.color_palette())
values = []
X = [ObjectId(oid).generation_time for oid, rec in data][:40]
ref = ""
current_color = cyclic_palette.next()
for oid, rec in data:
    if rec['media']['__data__'] != ref:
        current_color = cyclic_palette.next()
    values.append(current_color)
    ref = rec['media']['__data__']

values = values[:40]
    
fig, ax = plt.subplots(figsize=(18,2))
ax.scatter(X, [1]*len(X), c=values,
           marker='s', s=100)
fig.autofmt_xdate()

# everything after this is turning off stuff that's plotted by default

ax.yaxis.set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')

ax.get_yaxis().set_ticklabels([])
interval = timedelta(minutes=0)
plt.xlim(X[0] - interval, X[-1] + interval)
plt.title("Unique Images Captured via Polling")
plt.show()