band rms

Author: SaraPido

sigpro/aggregations/frequency/band.py

@@ -4,6 +4,7 @@
 
 
 def band_mean(amplitude_values, frequency_values, min_frequency, max_frequency):
+
     """Compute the mean values for a specific band.
 
     Filter between a high and low band and compute the mean value for this specific band.
@@ -22,9 +23,46 @@ def band_mean(amplitude_values, frequency_values, min_frequency, max_frequency):
         float:
             Mean value for the given band.
     """
+    
     lower_frequency_than = frequency_values <= max_frequency
     higher_frequency_than = frequency_values >= min_frequency
     selected_idx = np.where(higher_frequency_than & lower_frequency_than)
     selected_values = amplitude_values[selected_idx]
 
     return np.mean(selected_values)
+
+
+
+def band_rms(amplitude_values, frequency_values, min_frequency, max_frequency):
+
+    """Compute the rms values for a specific band.
+
+    Filter between a high and low band (inclusive) and compute the rms value for this
+    specific band.
+
+    Args:
+        amplitude_values (np.ndarray):
+            A numpy array with the signal values.
+        frequency_values (np.ndarray):
+            A numpy array with the frequency values.
+        min_frequency (int or float):
+            Band minimum.
+        max_frequency (int or float):
+            Band maximum.
+
+    Returns:
+        float:
+            rms value for the given band.
+    """
+
+    lower_frequency_than = frequency_values <= max_frequency
+    higher_frequency_than = frequency_values >= min_frequency
+
+    selected_idx = np.ravel(np.where(np.ravel(higher_frequency_than & lower_frequency_than)))
+
+    selected_idx = [int(x) for x in selected_idx]
+
+    selected_values = np.array(amplitude_values)[selected_idx]
+
+    return np.sqrt(np.mean(np.square(selected_values)))
+

sigpro/basic_primitives.py

@@ -177,3 +177,22 @@ def __init__(self, min_frequency, max_frequency):
             'min_frequency': min_frequency, 'max_frequency': max_frequency})
         self.set_fixed_hyperparameters({'min_frequency': {'type': 'float'},
                                         'max_frequency': {'type': 'float'}})
+
+class BandRMS(primitive.FrequencyAggregation):
+    """
+    BandMean primitive class.
+
+    Filters between a high and low band and compute the mean value for this specific band.
+
+    Args:
+        min_frequency (int or float):
+            Band minimum.
+        max_frequency (int or float):
+            Band maximum.
+    """
+
+    def __init__(self, min_frequency, max_frequency):
+        super().__init__('sigpro.aggregations.frequency.band.band_rms', init_params={
+            'min_frequency': min_frequency, 'max_frequency': max_frequency})
+        self.set_fixed_hyperparameters({'min_frequency': {'type': 'float'},
+                                        'max_frequency': {'type': 'float'}})

sigpro/primitives/sigpro/aggregations/frequency/band/band_rms.json

@@ -0,0 +1,37 @@
+{
+    "name": "sigpro.aggregations.frequency.band.band_rms",
+    "primitive": "sigpro.aggregations.frequency.band.band_rms",
+    "classifiers": {
+        "type": "aggregation",
+        "subtype": "frequency"
+    },
+    "produce": {
+        "args": [
+            {
+                "name": "amplitude_values",
+                "type": "numpy.ndarray"
+            },
+            {
+                "name": "frequency_values",
+                "type": "numpy.ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "value",
+                "type": "float"
+            }
+        ]
+    },
+    "hyperparameters": {
+        "fixed": {
+            "min_frequency": {
+                "type": "float"
+            },
+            "max_frequency": {
+                "type": "float"
+            }
+        },
+        "tunable": {}
+    }
+}