Merge pull request #64 from mendhak/issue63-missing-cffi

[EVA-2020-02-2.git] / library / enviroplus / noise.py

diff --git a/library/enviroplus/noise.py b/library/enviroplus/noise.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..7b6d5e283d2990b345149d6f01369fbd8d88f4b6 100644 (file)
--- a/library/enviroplus/noise.py
+++ b/library/enviroplus/noise.py
@@ -0,0 +1,90 @@
+import sounddevice
+import numpy
+
+
+class Noise():
+    def __init__(self,
+                 sample_rate=16000,
+                 duration=0.5):
+        """Noise measurement.
+
+        :param sample_rate: Sample rate in Hz
+        :param duraton: Duration, in seconds, of noise sample capture
+
+        """
+
+        self.duration = duration
+        self.sample_rate = sample_rate
+
+    def get_amplitudes_at_frequency_ranges(self, ranges):
+        """Return the mean amplitude of frequencies in the given ranges.
+
+        :param ranges: List of ranges including a start and end range
+
+        """
+        recording = self._record()
+        magnitude = numpy.abs(numpy.fft.rfft(recording[:, 0], n=self.sample_rate))
+        result = []
+        for r in ranges:
+            start, end = r
+            result.append(numpy.mean(magnitude[start:end]))
+        return result
+
+    def get_amplitude_at_frequency_range(self, start, end):
+        """Return the mean amplitude of frequencies in the specified range.
+
+        :param start: Start frequency (in Hz)
+        :param end: End frequency (in Hz)
+
+        """
+        n = self.sample_rate // 2
+        if start > n or end > n:
+            raise ValueError("Maxmimum frequency is {}".format(n))
+
+        recording = self._record()
+        magnitude = numpy.abs(numpy.fft.rfft(recording[:, 0], n=self.sample_rate))
+        return numpy.mean(magnitude[start:end])
+
+    def get_noise_profile(self,
+                          noise_floor=100,
+                          low=0.12,
+                          mid=0.36,
+                          high=None):
+        """Returns a noise charateristic profile.
+
+        Bins all frequencies into 3 weighted groups expressed as a percentage of the total frequency range.
+
+        :param noise_floor: "High-pass" frequency, exclude frequencies below this value
+        :param low: Percentage of frequency ranges to count in the low bin (as a float, 0.5 = 50%)
+        :param mid: Percentage of frequency ranges to count in the mid bin (as a float, 0.5 = 50%)
+        :param high: Optional percentage for high bin, effectively creates a "Low-pass" if total percentage is less than 100%
+
+        """
+
+        if high is None:
+            high = 1.0 - low - mid
+
+        recording = self._record()
+        magnitude = numpy.abs(numpy.fft.rfft(recording[:, 0], n=self.sample_rate))
+
+        sample_count = (self.sample_rate // 2) - noise_floor
+
+        mid_start = noise_floor + int(sample_count * low)
+        high_start = mid_start + int(sample_count * mid)
+        noise_ceiling = high_start + int(sample_count * high)
+
+        amp_low = numpy.mean(magnitude[noise_floor:mid_start])
+        amp_mid = numpy.mean(magnitude[mid_start:high_start])
+        amp_high = numpy.mean(magnitude[high_start:noise_ceiling])
+        amp_total = (amp_low + amp_mid + amp_high) / 3.0
+
+        return amp_low, amp_mid, amp_high, amp_total
+
+    def _record(self):
+        return sounddevice.rec(
+            int(self.duration * self.sample_rate),
+            samplerate=self.sample_rate,
+            blocking=True,
+            channels=1,
+            dtype='float64'
+        )