2024-05-02 20:06:47 +02:00
|
|
|
import requests
|
|
|
|
import matplotlib.pyplot as plt
|
|
|
|
from datetime import datetime
|
2024-05-03 22:39:30 +02:00
|
|
|
import json
|
2024-05-02 20:06:47 +02:00
|
|
|
|
2024-05-03 22:39:30 +02:00
|
|
|
|
|
|
|
file_path = "history.json"
|
2024-05-02 20:06:47 +02:00
|
|
|
history_url = "http://192.168.178.84:9587/bettwaage/history"
|
|
|
|
|
2024-05-03 22:39:30 +02:00
|
|
|
convert_time_to_seconds = True
|
2024-05-02 20:06:47 +02:00
|
|
|
|
2024-05-03 22:39:30 +02:00
|
|
|
# Script
|
|
|
|
data = None
|
2024-05-02 20:06:47 +02:00
|
|
|
|
2024-05-03 22:39:30 +02:00
|
|
|
if file_path is None:
|
|
|
|
data = requests.get(history_url)
|
|
|
|
data = data.json()
|
|
|
|
else:
|
|
|
|
with open(file_path, "r") as fp:
|
|
|
|
data = json.load(fp)
|
2024-05-02 20:06:47 +02:00
|
|
|
|
2024-05-06 01:03:34 +02:00
|
|
|
|
|
|
|
# Experiment: Solving for missing foot with known total weight
|
|
|
|
bed_weight = 78290
|
|
|
|
person_weight = 63000
|
|
|
|
known_total_weight = bed_weight + person_weight
|
|
|
|
bed_only_weight = {}
|
|
|
|
for d in data:
|
|
|
|
if d["total"] == bed_weight:
|
|
|
|
bed_only_weight = {
|
|
|
|
"tl": d["tl"],
|
|
|
|
"tr": d["tr"],
|
|
|
|
"bl": bed_weight - (d["tl"] + d["tr"] + d["br"]),
|
|
|
|
"br": d["br"],
|
|
|
|
}
|
|
|
|
break
|
|
|
|
|
|
|
|
in_bed_data = None
|
|
|
|
threshhold = 100000
|
|
|
|
min_length = 100
|
|
|
|
for d in data:
|
|
|
|
t = d["total"]
|
|
|
|
if t >= threshhold:
|
|
|
|
if in_bed_data is None:
|
|
|
|
in_bed_data = []
|
|
|
|
in_bed_data.append(d)
|
|
|
|
elif in_bed_data is not None:
|
|
|
|
if len(in_bed_data) < min_length:
|
|
|
|
in_bed_data = []
|
|
|
|
else:
|
|
|
|
break
|
|
|
|
|
|
|
|
|
|
|
|
# Calculate bottom left
|
|
|
|
for d in data:
|
|
|
|
d["bl"] = known_total_weight - (d["br"] + d["tr"] + d["tl"])
|
|
|
|
# Set known total weight
|
|
|
|
d["total"] = known_total_weight
|
|
|
|
|
|
|
|
|
|
|
|
data = in_bed_data
|
|
|
|
|
|
|
|
|
|
|
|
# Array data
|
2024-05-03 22:39:30 +02:00
|
|
|
x = [d["timestamp"] for d in data]
|
|
|
|
x = [datetime.strptime(d, "%Y-%m-%d %H:%M:%S.%f") for d in x]
|
|
|
|
|
|
|
|
if convert_time_to_seconds:
|
|
|
|
max_time = max(x)
|
|
|
|
x = [(d - max_time).total_seconds() for d in x]
|
2024-05-02 20:06:47 +02:00
|
|
|
|
|
|
|
total = [d["total"] for d in data]
|
|
|
|
tl = [d["tl"] for d in data]
|
|
|
|
tr = [d["tr"] for d in data]
|
|
|
|
bl = [d["bl"] for d in data]
|
|
|
|
br = [d["br"] for d in data]
|
2024-05-06 01:03:34 +02:00
|
|
|
top = [l + r for l, r in zip(tl, tr)]
|
|
|
|
bottom = [l + r for l, r in zip(bl, br)]
|
|
|
|
left = [t + b for t, b in zip(tl, bl)]
|
|
|
|
right = [t + b for t, b in zip(tr, br)]
|
|
|
|
|
|
|
|
|
|
|
|
# Experiment: Calculate position over time
|
|
|
|
bed_size = (140, 200)
|
|
|
|
left_bed_only = bed_only_weight["tl"] + bed_only_weight["bl"]
|
|
|
|
top_bed_only = bed_only_weight["tr"] + bed_only_weight["tl"]
|
|
|
|
position_over_time = []
|
|
|
|
for t, l in zip(top, left):
|
|
|
|
position_over_time.append(
|
|
|
|
(
|
|
|
|
bed_size[0] * (l - left_bed_only) / person_weight,
|
|
|
|
bed_size[1] * (t - top_bed_only) / person_weight,
|
|
|
|
)
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# Plot data
|
|
|
|
fig, (ax0, ax1) = plt.subplots(nrows=2)
|
|
|
|
|
|
|
|
ax0.set_xlabel("Time (s)")
|
|
|
|
ax0.set_ylabel("Weight (kg)")
|
|
|
|
|
|
|
|
ax0.plot(x, total, color="tab:blue")
|
|
|
|
ax0.plot(x, tl, color="tab:red")
|
|
|
|
ax0.plot(x, tr, color="tab:green")
|
|
|
|
ax0.plot(x, bl, color="tab:orange")
|
|
|
|
ax0.plot(x, br, color="tab:purple")
|
|
|
|
ax0.plot(x, top, color="tab:pink")
|
|
|
|
ax0.plot(x, bottom, color="tab:grey")
|
2024-05-02 20:06:47 +02:00
|
|
|
|
2024-05-06 01:03:34 +02:00
|
|
|
ax0.legend(
|
|
|
|
["Total", "Top Left", "Top Right", "Bottom Left", "Bottom Right", "Top", "Bottom"]
|
|
|
|
)
|
2024-05-02 20:06:47 +02:00
|
|
|
|
|
|
|
|
2024-05-06 01:03:34 +02:00
|
|
|
# Experiment: Plot position
|
|
|
|
import math
|
2024-05-02 20:06:47 +02:00
|
|
|
|
2024-05-06 01:03:34 +02:00
|
|
|
segments = 100
|
|
|
|
seg_length = math.ceil(len(position_over_time) / segments)
|
|
|
|
horizontal, vertical = zip(*position_over_time)
|
|
|
|
for i in range(0, segments):
|
|
|
|
low = int(i * seg_length)
|
|
|
|
high = min(int((i + 1) * seg_length), len(position_over_time))
|
|
|
|
ax1.plot(
|
|
|
|
horizontal[low:high],
|
|
|
|
vertical[low:high],
|
|
|
|
color=(0.0, 0.5, i / segments),
|
|
|
|
linewidth=0.3,
|
|
|
|
)
|
|
|
|
ax1.set_xlim((0, bed_size[0]))
|
|
|
|
ax1.set_ylim((0, bed_size[1]))
|
|
|
|
ax1.invert_yaxis()
|
2024-05-02 20:06:47 +02:00
|
|
|
|
|
|
|
|
|
|
|
plt.show()
|