inline computation results in the return statement directly

Frix-x · Nov 23, 2024 · e7f3180 · e7f3180
1 parent 167bcb3
commit e7f3180
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Showing 5 changed files with 5 additions and 18 deletions.
diff --git a/shaketune/graph_creators/axes_map_graph_creator.py b/shaketune/graph_creators/axes_map_graph_creator.py
@@ -138,8 +138,7 @@ def compute(self):
         formatted_direction_vector = self._format_direction_vector(direction_vectors)
         ConsoleOutput.print(f'--> Detected axes_map: {formatted_direction_vector}')
 
-        # Prepare data for plotting
-        computation_result = {
+        return {
             'acceleration_data_0': [d[0] for d in acceleration_data],
             'acceleration_data_1': [d[1] for d in acceleration_data],
             'gravity': gravity,
@@ -153,8 +152,6 @@ def compute(self):
             'st_version': self.st_version,
         }
 
-        return computation_result
-
     def _wavelet_denoise(self, data: np.ndarray, wavelet: str = 'db1', level: int = 1) -> Tuple[np.ndarray, np.ndarray]:
         coeffs = pywt.wavedec(data, wavelet, mode='smooth')
         threshold = np.median(np.abs(coeffs[-level])) / 0.6745 * np.sqrt(2 * np.log(len(data)))

diff --git a/shaketune/graph_creators/belts_graph_creator.py b/shaketune/graph_creators/belts_graph_creator.py
@@ -120,8 +120,7 @@ def compute(self):
             mhi = self._compute_mhi(similarity_factor, signal1, signal2)
             ConsoleOutput.print(f'Mechanical health: {mhi}')
 
-        # Prepare data for plotting
-        computation_result = {
+        return {
             'signal1': signal1,
             'signal2': signal2,
             'similarity_factor': similarity_factor,
@@ -135,8 +134,6 @@ def compute(self):
             'max_freq': self.max_freq,
         }
 
-        return computation_result
-
     def _compute_signal_data(self, data: np.ndarray, common_freqs: np.ndarray, max_freq: float):
         helper = get_shaper_calibrate_module().ShaperCalibrate(printer=None)
         calibration_data = helper.process_accelerometer_data(data)

diff --git a/shaketune/graph_creators/shaper_graph_creator.py b/shaketune/graph_creators/shaper_graph_creator.py
@@ -196,8 +196,7 @@ def compute(self):
             shaper_choices = [klipper_shaper_choice.upper()]
             ConsoleOutput.print(f'{shaper_string} (with a damping ratio of {zeta:.3f})')
 
-        # And finally setup the results to return them
-        computation_result = {
+        return {
             'measurements': self.measurements,
             'compat': compat,
             'max_smoothing_computed': max_smoothing_computed,
@@ -221,8 +220,6 @@ def compute(self):
             'st_version': self.st_version,
         }
 
-        return computation_result
-
     # Find the best shaper parameters using Klipper's official algorithm selection with
     # a proper precomputed damping ratio (zeta) and using the configured printer SQV value
     # This function also sweep around the smoothing values to help you find the best compromise

diff --git a/shaketune/graph_creators/static_graph_creator.py b/shaketune/graph_creators/static_graph_creator.py
@@ -78,7 +78,7 @@ def compute(self):
         pdata, bins, t = compute_spectrogram(datas[0])
         del datas
 
-        computation_result = {
+        return {
             'freq': self.freq,
             'duration': self.duration,
             'accel_per_hz': self.accel_per_hz,
@@ -89,5 +89,3 @@ def compute(self):
             'pdata': pdata,
             'max_freq': self.max_freq,
         }
-
-        return computation_result
diff --git a/shaketune/graph_creators/vibrations_graph_creator.py b/shaketune/graph_creators/vibrations_graph_creator.py
@@ -211,7 +211,7 @@ def compute(self):
                 f'Motors have a main resonant frequency at {motor_fr:.1f}Hz but it was impossible to estimate a damping ratio.'
             )
 
-        computation_results = {
+        return {
             'measurements': self.measurements,
             'all_speeds': all_speeds,
             'all_angles': all_angles,
@@ -241,8 +241,6 @@ def compute(self):
             'st_version': self.st_version,
         }
 
-        return computation_results
-
     # Calculate motor frequency profiles based on the measured Power Spectral Density (PSD) measurements for the machine kinematics
     # main angles and then create a global motor profile as a weighted average (from their own vibrations) of all calculated profiles
     def _compute_motor_profiles(