finish mov_avg

This commit is contained in:
2025-04-25 19:49:46 +02:00
parent 58ebddcd48
commit a7091af1b2
5 changed files with 58 additions and 9 deletions

View File

@@ -46,6 +46,8 @@ class FilterDevApp(tk.Tk):
self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH)
NavigationToolbar2Tk(self.canvas, self.frame)
self.focus_force()
def update_plot(self):
if self.filter is None:
return

View File

@@ -3,4 +3,11 @@ SERVICE_UUID = "9f0dfdb2-e978-494c-8f15-68dbe8d28672"
MILLIS_UUID = "abb92561-a809-453c-8c7c-71d3fff5b86e"
WEIGHT_UUID = "123e4567-e89b-12d3-a456-426614174000"
DEFAULT_CALIB = 307333.83
DEFAULT_CALIB = 307333.83
MOV_AVG_DEFAULTS = {
"window_size": 10,
"decimals": 1,
"reset_threshold": 0.5,
"ignore_samples": 2
}

View File

@@ -33,7 +33,7 @@ class Filter:
calib_factor = 100. / float(self.calib_entry.get())
df = self.device.data
df = df[df['weights'] < 10e9]
df = df[df['weights'] < 10e8]
df['timestamps'] -= df['timestamps'].min()
df['filtered'], df['filtered_calib'] = self.filter(df, calib_factor)

View File

@@ -1,21 +1,58 @@
from tkinter import ttk
from statistics import mean
import pandas as pd
from .base import Filter
from ..gui import Slider
from ..config import MOV_AVG_DEFAULTS
class MovAvg(Filter):
def init_params(self, toolbar):
self.param_map = {
"window_size": Slider(toolbar, "Window Size", 1, 100, 10, self.callback),
"decimals": Slider(toolbar, "Decimals", 1, 5, 1, self.callback),
# "reset_threshold": Slider(self.toolbar, "Reset Threshold", 0.001, 0.1, 0.1, self.update),
"window_size": Slider(toolbar, "Window Size", 1, 100,
MOV_AVG_DEFAULTS['window_size'],
self.callback),
"decimals": Slider(toolbar, "Decimals", 1, 5,
MOV_AVG_DEFAULTS['decimals'],
self.callback),
"reset_threshold": Slider(toolbar, "Reset Threshold", 0.01, 1,
MOV_AVG_DEFAULTS['reset_threshold'],
self.callback, float),
"ignore_samples": Slider(toolbar, "Ignore Samples before reset", 1, 10,
MOV_AVG_DEFAULTS['ignore_samples'],
self.callback)
}
def filter(self, df: pd.DataFrame, calib_factor: float) -> pd.Series:
params = self._get_params()
mov_avg = df['weights'].rolling(window=int(params['window_size'])).mean()
reset_threshold = params['reset_threshold'] / calib_factor
window = []
mov_avg = []
ignored_samples = 0
for w in df['weights']:
if len(window) < params['window_size']:
window.append(w)
mov_avg.append(mean(window))
else:
out_of_threshold = abs(mov_avg[-1] - w) > reset_threshold
if out_of_threshold and\
ignored_samples < params['ignore_samples']:
ignored_samples += 1
mov_avg.append(mov_avg[-1])
elif out_of_threshold:
ignored_samples = 0
window = [w]
mov_avg.append(w)
else:
ignored_samples = 0
window.append(w)
mov_avg.append(mean(window))
mov_avg = pd.Series(mov_avg)
mov_avg_calib = (mov_avg * calib_factor).round(int(params['decimals']))
return mov_avg, mov_avg_calib

View File

@@ -6,8 +6,10 @@ class Slider:
label_text,
from_, to,
initial_value,
command):
command,
dtype=int):
self.command = command
self.dtype = dtype
self.label = ttk.Label(parent, text=f"{label_text}: {int(initial_value)}")
@@ -16,7 +18,8 @@ class Slider:
def update(self, event=None):
value = self.slider.get()
self.label.config(text=f"{self.label.cget('text').split(':')[0]}: {int(value)}")
value_str = int(value) if self.dtype == int else f'{value:.02f}'
self.label.config(text=f"{self.label.cget('text').split(':')[0]}: {value_str}")
self.command()
def get_value(self):