cumulative_interp.px

import json
from pprint import pprint
import time
from datetime import date,datetime,timedelta
from numpy import arange

import matplotlib.pyplot as plt
from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange, date2num
from matplotlib.markers import MarkerStyle
import numpy as np
import plotly.plotly as py
from scipy import interpolate
from scipy.interpolate import spline
from operator import itemgetter


data = json.load(open('hr.json'))
times = []
bpms = []

tps = [];

k = 0
for i in data["insertedDataPoint"]:
 time=datetime.fromtimestamp(round(int(i["startTimeNanos"])/10e8))
 value=i["value"][0]["fpVal"]
 tps.append((time, value));

tps.sort(key=itemgetter(0))

for i in tps:
 times.append(i[0])
 bpms.append(i[1])
    
dates = date2num(times)

def running_mean(x, N):
    cumsum = np.cumsum(np.insert(x, 0, 0)) 
    return (cumsum[N:] - cumsum[:-N]) / float(N)

n = 120
rm = running_mean(dates, n)
rm_y = running_mean(bpms, n)

f = interpolate.interp1d(rm, rm_y, kind='slinear', assume_sorted=False)


print(rm);

fig, ax = plt.subplots(figsize=(20,10))
if False:
    ax.plot_date(dates,
                 bpms,
                 color='#2980b9',
                 marker='o',
                 markersize=1,
                 markeredgecolor='#3498db',
                 linestyle='solid'           
    )
else:
    ax.plot_date(rm,
             f(rm),
             color='#d35400',
             marker='o',
             markersize=1,
             markeredgecolor='#e67e22',
             linestyle='solid',
    )
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_minor_locator(HourLocator(arange(0, 25, 12)))
ax.xaxis.set_major_formatter(DateFormatter('%d.%m'))
ax.fmt_xdata = DateFormatter('%Y-%m-%d %H:%M:%S')
plt.title('Hearth rate over time');
plt.gcf().autofmt_xdate()
plt.savefig('plot.png')
plt.show()