init workflow

filter_dev/filter/base.py

@@ -3,8 +3,8 @@ from typing import Dict
 import pandas as pd
 from tkinter.ttk import Frame
 
-from ..gui.device import Device
-from ..gui.slider import Slider
+from ..gui import Device, Slider, Entry
+from ..config import DEFAULT_CALIB
 
 class Filter:
 
@@ -15,6 +15,8 @@ class Filter:
         self.toolbar = toolbar
         self.callback = callback
 
+        self.calib_entry = Entry(toolbar, "Calibration Factor", DEFAULT_CALIB)
+
         self.init_params(toolbar)
 
     def init_params(self, toolbar):
@@ -28,17 +30,21 @@ class Filter:
         return params
     
     def __call__(self) -> pd.DataFrame:
+        calib_factor = 100. / float(self.calib_entry.get())
         df = self.device.data
-        df['filtered'] = self.filter(df)
+        df['filtered'], df['filtered_calib'] = self.filter(df, calib_factor)
+        df['calib_weights'] = df['weights'] * calib_factor
         return df
     
-    def filter(self, df: pd.DataFrame) -> pd.Series:
+    def filter(self, df: pd.DataFrame, calib_factor: float) -> pd.Series:
         raise NotImplementedError()
     
-    def pack(self):
+    def pack(self, **kwargs):
+        self.calib_entry.pack(**kwargs)
         for k, v in self.param_map.items():
-            v.pack()
+            v.pack(**kwargs)
 
     def pack_forget(self):
+        self.calib_entry.pack_forget()
         for k, v in self.param_map.items():
             v.pack_forget()

filter_dev/filter/mov_avg.py

@@ -1,18 +1,21 @@
+from tkinter import ttk
+
 import pandas as pd
 
 from .base import Filter
-from ..gui.slider import Slider
+from ..gui import Slider
 
 class MovAvg(Filter):
     
     def init_params(self, toolbar):
         self.param_map = {
-            "window_size": Slider(toolbar, "Window Size", 1, 500, 10, self.callback),
+            "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),
         }
     
-    def filter(self, df: pd.DataFrame) -> pd.Series:
+    def filter(self, df: pd.DataFrame, calib_factor: float) -> pd.Series:
         params = self._get_params()
-        return df['weights'].rolling(window=int(params['window_size'])).mean()\
-                            .round(int(params['decimals']))
+        mov_avg = df['weights'].rolling(window=int(params['window_size'])).mean()
+        mov_avg_calib = (mov_avg * calib_factor).round(int(params['decimals']))
+        return mov_avg, mov_avg_calib