finish mov_avg
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@@ -46,6 +46,8 @@ class FilterDevApp(tk.Tk):
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self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH)
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NavigationToolbar2Tk(self.canvas, self.frame)
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self.focus_force()
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def update_plot(self):
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if self.filter is None:
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return
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@@ -4,3 +4,10 @@ MILLIS_UUID = "abb92561-a809-453c-8c7c-71d3fff5b86e"
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WEIGHT_UUID = "123e4567-e89b-12d3-a456-426614174000"
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DEFAULT_CALIB = 307333.83
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MOV_AVG_DEFAULTS = {
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"window_size": 10,
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"decimals": 1,
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"reset_threshold": 0.5,
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"ignore_samples": 2
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}
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@@ -33,7 +33,7 @@ class Filter:
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calib_factor = 100. / float(self.calib_entry.get())
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df = self.device.data
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df = df[df['weights'] < 10e9]
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df = df[df['weights'] < 10e8]
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df['timestamps'] -= df['timestamps'].min()
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df['filtered'], df['filtered_calib'] = self.filter(df, calib_factor)
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@@ -1,21 +1,58 @@
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from tkinter import ttk
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from statistics import mean
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import pandas as pd
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from .base import Filter
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from ..gui import Slider
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from ..config import MOV_AVG_DEFAULTS
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class MovAvg(Filter):
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def init_params(self, toolbar):
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self.param_map = {
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"window_size": Slider(toolbar, "Window Size", 1, 100, 10, self.callback),
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"decimals": Slider(toolbar, "Decimals", 1, 5, 1, self.callback),
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# "reset_threshold": Slider(self.toolbar, "Reset Threshold", 0.001, 0.1, 0.1, self.update),
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"window_size": Slider(toolbar, "Window Size", 1, 100,
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MOV_AVG_DEFAULTS['window_size'],
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self.callback),
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"decimals": Slider(toolbar, "Decimals", 1, 5,
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MOV_AVG_DEFAULTS['decimals'],
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self.callback),
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"reset_threshold": Slider(toolbar, "Reset Threshold", 0.01, 1,
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MOV_AVG_DEFAULTS['reset_threshold'],
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self.callback, float),
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"ignore_samples": Slider(toolbar, "Ignore Samples before reset", 1, 10,
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MOV_AVG_DEFAULTS['ignore_samples'],
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self.callback)
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}
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def filter(self, df: pd.DataFrame, calib_factor: float) -> pd.Series:
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params = self._get_params()
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mov_avg = df['weights'].rolling(window=int(params['window_size'])).mean()
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reset_threshold = params['reset_threshold'] / calib_factor
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window = []
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mov_avg = []
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ignored_samples = 0
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for w in df['weights']:
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if len(window) < params['window_size']:
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window.append(w)
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mov_avg.append(mean(window))
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else:
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out_of_threshold = abs(mov_avg[-1] - w) > reset_threshold
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if out_of_threshold and\
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ignored_samples < params['ignore_samples']:
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ignored_samples += 1
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mov_avg.append(mov_avg[-1])
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elif out_of_threshold:
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ignored_samples = 0
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window = [w]
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mov_avg.append(w)
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else:
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ignored_samples = 0
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window.append(w)
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mov_avg.append(mean(window))
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mov_avg = pd.Series(mov_avg)
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mov_avg_calib = (mov_avg * calib_factor).round(int(params['decimals']))
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return mov_avg, mov_avg_calib
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@@ -6,8 +6,10 @@ class Slider:
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label_text,
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from_, to,
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initial_value,
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command):
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command,
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dtype=int):
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self.command = command
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self.dtype = dtype
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self.label = ttk.Label(parent, text=f"{label_text}: {int(initial_value)}")
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@@ -16,7 +18,8 @@ class Slider:
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def update(self, event=None):
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value = self.slider.get()
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self.label.config(text=f"{self.label.cget('text').split(':')[0]}: {int(value)}")
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value_str = int(value) if self.dtype == int else f'{value:.02f}'
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self.label.config(text=f"{self.label.cget('text').split(':')[0]}: {value_str}")
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self.command()
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def get_value(self):
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