feat: добавил фильтрацию сигналов

data/path.json

@@ -3,12 +3,14 @@
     "source": "/home/smart/PN/TWC/trace/Weld Point",
     "buffer": "/home/andrei/PycharmProjects/traceDelivery/buffer",
     "destination": "/home/andrei/Desktop/bla",
-    "apply_filter": false
+    "apply_filter": true,
+    "sigma_filter": 3
   },
   {
     "source": "/home/smart/PN/TWC/trace/test1",
     "buffer": "/home/andrei/PycharmProjects/traceDelivery/buffer",
     "destination": "/home/andrei/Desktop/bla",
-    "apply_filter": false
+    "apply_filter": false,
+    "sigma_filter": 3
   }
 ]

requirements.txt

@@ -1,13 +1,22 @@
 bcrypt==4.2.0
 cffi==1.17.1
+contourpy==1.3.1
 cryptography==43.0.3
+cycler==0.12.1
+fonttools==4.54.1
+kiwisolver==1.4.7
 loguru==0.7.2
+matplotlib==3.9.2
 numpy==2.1.3
+packaging==24.2
 pandas==2.2.3
 paramiko==3.5.0
+pillow==11.0.0
 pycparser==2.22
 PyNaCl==1.5.0
+pyparsing==3.2.0
 python-dateutil==2.9.0.post0
 pytz==2024.2
+scipy==1.14.1
 six==1.16.0
 tzdata==2024.2

src/subscription/subscription_object.py

@@ -6,3 +6,4 @@ class SubscriptionObject(NamedTuple):
     buffer: str
     destination: str
     apply_filter: bool
+    sigma_filter: int

src/tuning/tools.py

@@ -1,7 +1,12 @@
+import pandas as pd
+from scipy.ndimage import gaussian_filter1d
 
-def filter_signal(signal_name: str):
-    ...
 
+def filter_signal(dataframe: pd.DataFrame, sigma: int, *signal_names: str) -> pd.DataFrame:
+    for name in signal_names:
+        smoothed_signal = gaussian_filter1d(dataframe[name], sigma)
+        dataframe[name] = smoothed_signal
+    return dataframe
 
 def move_signal(signal_name: str):
     ...

src/tuning/tuner.py

@@ -1,9 +1,11 @@
-from os import path
+from os import path, remove
 import shutil
 
+import pandas as pd
+
 from tuning.abstraction import AbstractTuner
 from subscription.subscription_object import SubscriptionObject
-import tuning.tools
+import tuning.tools as tools
 
 
 class Tuner(AbstractTuner):
@@ -11,11 +13,18 @@ class Tuner(AbstractTuner):
     def accept(self, subscriber) -> None:
         subscriber.tuner = self
 
+    @staticmethod
+    def _tune(filepath: str, sigma: int):
+        df = pd.read_csv(filepath)
+        df_filtered = tools.filter_signal(df, sigma, "Electrode Force, N ME", "Electrode Force, N FE")
+        remove(filepath)
+        df_filtered.to_csv(filepath)
+
     def notify(self, obj: SubscriptionObject, filename: str):
         local_path = path.join(obj.buffer, filename)
         destination_path = path.join(obj.destination, filename)
 
         if obj.apply_filter:
-            ...
-        else:
-            shutil.move(src=local_path, dst=destination_path)
+            self._tune(local_path, obj.sigma_filter)
+
+        shutil.move(src=local_path, dst=destination_path)