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julian-klode/Figure.svg

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Figure.svg
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<dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
<dc:date>2023-03-26T10:16:25.555678</dc:date>
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HRV-Fit.ipynb
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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raise ValueError("Out of date")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raise ValueError("Out of date")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])
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