Generates smoothed weight changed statistics using exponentially-weighted moving average.

weight.py

import dateutil
import pandas as pd
import random
from os.path import expanduser, join
home = expanduser("~")


with open(join(home, "Dropbox/Text Notes/Weight.txt"), "r") as f:
    lines = f.readlines()


def parse_line(line):
    s = line.split(" ")
    weight = float(s[0])
    date = dateutil.parser.parse(' '.join(s[1:4]))
    return date, weight

weight = pd.DataFrame([parse_line(l) for l in lines], columns=["date", "weight"]) \
    .set_index("date") \
    .resample("1D", how="mean")
weight["missing"] = weight.weight.isnull()
weight.weight = weight.weight.interpolate(method="linear")
std = weight.weight.diff().dropna().std()
noise = weight.missing.map(lambda missing: random.normalvariate(0, std) if missing else 0)
weight.weight = weight.weight + noise

smoothed = pd.ewma(weight.weight, span=30)
current = smoothed[-1]
stats = """
Weight (lbs): %(weight).1f
Total Δ: %(total).1f
1 Week Δ: %(week).1f
1 Month Δ: %(month).1f
1 Year Δ: %(year).1f
""".strip() % {"weight": current,
               "total": current - smoothed[0],
               "week": current - smoothed[-8],
               "month": current - smoothed[-32],
               "year": current - smoothed[-366],
               }

with open(join(home, "Dropbox/Text Notes/Weight Stats.txt"), "w") as f:
    f.write(stats)