Generate graphs from the last week of UP data.

up_data_skyline_viz.py

import json
import pandas
import pylab as plt

#Load data
M = [json.loads(l) for l in file("data/snapshots.jl", 'r').readlines()]

#Day step graph

D = pandas.DataFrame(M[2]["data"])
D.columns = ["time", "steps"]
plt.vlines(D.time, D.steps, 0)
plt.savefig("graphs/daily-step-graph.png")
plt.show()

#Weekly cumulative step graphs, overlaid
for i,m in enumerate(M[:7]):
    D = pandas.DataFrame(m["data"])
    D.columns = ["time", "steps"]
    n = D.shape[0]

    plt.plot( range(n), D.steps.cumsum() )
    if i==2:
        plt.plot( range(n), D.steps.cumsum(), lw=4, c='black' )

plt.savefig("graphs/weekly-cumulative-steps-overlaid.png")
plt.show()

#Weekly cumulative step graphs, in sequence
for i,m in enumerate(M[:7]):
    D = pandas.DataFrame(m["data"])
    D.columns = ["time", "steps"]
    n = D.shape[0]

    plt.plot( D.time, D.steps.cumsum() )
    if i==2:
        plt.plot( D.time, D.steps.cumsum(), lw=4, c='black' )

plt.savefig("graphs/weekly-cumulative-steps-seq.png")
plt.show()

#Weekly skyline graph
total = 0
for i,m in enumerate(M[:7]):
    D = pandas.DataFrame(m["data"])
    D.columns = ["time", "steps"]
    n = D.shape[0]

    rev_range = range(total+n, total+2*n)
    rev_range.reverse()

    plt.plot( range(total, total+n), D.steps.cumsum(), c='black', lw=4 )
    plt.plot( rev_range, D.steps.cumsum(), c='black', lw=4 )
    total += 2*n

plt.savefig("graphs/weekly-skyline-graph.png")
plt.show()