Figure.svg

HRV-Fit.ipynb

HRV.ipynb

HRV.py

#!/usr/bin/env python
# coding: utf-8

# In[7]:


#!/usr/bin/python3
import json
import requests
import datetime
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
from matplotlib.ticker import FormatStrFormatter


VIEW = int(sys.argv[1]) if len(sys.argv) > 1 else 60
SCORE = True
today = datetime.date.today()
start = (today - datetime.timedelta(days=VIEW + 80))
first = start + datetime.timedelta(days=1)

url = "https://api.ouraring.com/v2/usercollection/sleep"
params = {
    "start_date": start.isoformat(),
    "end_date": today.isoformat(),
}
headers = {
    "Authorization": "Bearer " + open("/home/jak/Private/oura-token").read().strip()
}
try:
    with open("/home/jak/.cache/oura.json") as cached:
        data = json.load(cached)
    if datetime.date.fromisoformat(data["data"][-1]["day"]) < today:
        raise ValueError("Out of date")
    if datetime.date.fromisoformat(data["data"][0]["day"]) > first:
        raise ValueError("Out of date")
except (ValueError, FileNotFoundError) as e:
    print("Fetching due to error:", e)
    response = requests.request("GET", url, headers=headers, params=params)
    data = response.json()
    with open("/home/jak/.cache/oura.json", "w") as cached:
        json.dump(data, cached)


dates = []
hrs = []
hrvs = []
for sleep in data["data"]:
    if sleep["type"] != "long_sleep":
        continue
    dates.append(datetime.date.fromisoformat(sleep["day"]))
    # hrs.append(sleep["average_heart_rate"])
    hrs.append(np.round(np.mean([x for x in sleep["heart_rate"]["items"] if x])))
    if SCORE:
        if sleep["average_hrv"]:
            #hrvs.append(np.round(np.log(sleep["average_hrv"]), 1))
            #hrvs.append(np.round(1.7 * np.log(sleep["average_hrv"]) + 1, 1))
            #hrvs.append(np.round(1.75202 * np.log(sleep["average_hrv"] * 1.57691), 1))
            hrvs.append(np.round(1.73677755 * np.log(sleep["average_hrv"] * 1.63747424), 1))
            #1.73677755, 1.63747424
        else:
            hrvs.append(None)
    else:
        hrvs.append(sleep["average_hrv"])


def normal_range(values):
    rolling = values.dropna().rolling(window=60)
    mean = rolling.mean()
    std = rolling.std()
    return (mean - std), mean, (mean + std)


def plot_hrvs(hrvs, label):
    # some sample data
    ts = pd.Series(hrvs, index=dates)

    digits = 0
    if label == "HRV" and SCORE:
        digits = 1
    if "coeff" in label:
        digits = 2

    rolling = ts.dropna().rolling(window=7)
    mean = rolling.mean()
    min_range, mean_range, max_range = (r.round(digits) for r in normal_range(ts))

    coeff = rolling.std() / rolling.mean() * 100
    min_coeff_range, mean_coeff_range, max_coeff_range = normal_range(coeff)

    table = (
        ts.to_frame(name="HRV")
        .join(min_range.round(digits).to_frame(name="MIN"))
        .join(max_range.round(digits).to_frame(name="MAX"))
        .join(mean.to_frame(name="MEAN"))
    )
    table_coeff = (
        coeff.round(1).to_frame(name="Coeff")
        .join(min_coeff_range.round(1).to_frame(name="MIN"))
        .join(max_coeff_range.round(1).to_frame(name="MAX"))
    )

    if "coeff" in label:
        coeff.plot(label=label)
        max_coeff_range.plot(label="MAX", color="green")
        min_coeff_range.plot(label="MIN", color="red")
    else:
        ts.plot(style="o--", color="#" + ("c" * 6), zorder=1, label=label)
        rolling.mean().plot(style="-", label=f"{label} 7 day")
        #mean_range.plot(label=f"{label} 60 day", color="#333333", style="--", zorder=2)
        max_range.plot(label="MAX", color="green")
        min_range.plot(label="MIN", color="red")
    if "coeff" in label:
        return
    for index, entry in table.iterrows():
        if index == today or True:
            print(
                    label + ":      ",
                index,
                entry["HRV"],
                entry["MIN"],
                entry["MAX"],
                "" if entry["MIN"] < entry["HRV"] < entry["MAX"] else "(!)",
                sep="\t",
            )
    for index, entry in table_coeff.iterrows():
        if index == today:
            print(
                    label + " coeff:",
                index,
                entry["Coeff"],
                entry["MIN"],
                entry["MAX"],
                "" if entry["MIN"] < entry["Coeff"] < entry["MAX"] else "(!)",
                sep="\t",
            )

# In[9]:


#fig=plt.figure(figsize=(40, 3*25))
fig=plt.figure(figsize=(8.27*2,11.69*2)) # for landscape

fig.add_subplot(311, title="HRV")
plot_hrvs(hrvs, "HRV")
plt.xlim([today - datetime.timedelta(days=VIEW), today + datetime.timedelta(days=0)])
plt.gca().legend()
from matplotlib.ticker import AutoLocator, ScalarFormatter

if not SCORE:
    plt.gca().set_yscale('log')
plt.gca().yaxis.set_major_locator(AutoLocator())
plt.gca().yaxis.set_major_formatter(ScalarFormatter())
plt.gca().minorticks_off()

plt.grid()
# plt.show()


# In[4]:
fig.add_subplot(312, title="HRV coeff")
plot_hrvs(hrvs, "HRV coeff")
plt.xlim([today - datetime.timedelta(days=VIEW), today + datetime.timedelta(days=0)])
plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%d%%'))
plt.gca().legend()
plt.grid(axis="x")


#plt.figure(figsize=(38, 24))
fig.add_subplot(313, title="HR")
plot_hrvs(hrs, "HR")
plt.gca().legend()
plt.xlim([today - datetime.timedelta(days=VIEW), today + datetime.timedelta(days=0)])
plt.grid()

if VIEW >= 210:
    from matplotlib.dates import MO, MonthLocator
    for ax in plt.gcf().get_axes():
        ax.xaxis.set_major_locator(MonthLocator())
else:
    from matplotlib.dates import MO, WeekdayLocator
    for ax in plt.gcf().get_axes():
        ax.xaxis.set_major_locator(WeekdayLocator(byweekday=MO, interval=max(VIEW//70, 1)) )
fig.autofmt_xdate()
plt.text(0.05,0.95, f"{VIEW} day HRV analysis", transform=fig.transFigure, size=24)
plt.savefig("Figure.png",  orientation = 'portrait', format = 'png', dpi=300)
plt.savefig("Figure.pdf",  orientation = 'portrait', format = 'pdf', dpi=300)
plt.savefig("Figure.svg",  orientation = 'portrait', format = 'svg', dpi=300)
#plt.show()



# In[5]:


data["data"][-1]["average_heart_rate"]


# In[6]:


np.mean([x for x in data["data"][-1]["heart_rate"]["items"] if x])