ZH: move ac_* function definitions into lib/snr.py

simulations/airshower_beacon_simulation/lib/__init__.py

@@ -1,2 +1,3 @@
 from .lib import *
 from . import rit
+from .snr import *

simulations/airshower_beacon_simulation/lib/snr.py

@@ -0,0 +1,56 @@
+import numpy as np
+from collections import namedtuple
+
+passband = namedtuple("passband", ['low', 'high'], defaults=[0, np.inf])
+
+def get_freq_spec(val,dt):
+    """From earsim/tools.py"""
+    fval = np.fft.fft(val)[:len(val)//2]
+    freq =  np.fft.fftfreq(len(val),dt)[:len(val)//2]
+    return fval, freq
+
+
+def bandpass_samples(samples, samplerate, band=passband()):
+        """
+        Bandpass the samples with this passband.
+        This is a hard filter.
+        """
+        fft, freqs = get_freq_spec(samples, samplerate)
+
+        fft[ ~ self.freq_mask(freqs) ] = 0
+
+        return np.fft.irfft(fft)
+
+def bandpass_mask(freqs, band=passband()):
+    low_pass = abs(freqs) <= band[1]
+    high_pass = abs(freqs) >= band[0]
+
+    return low_pass & high_pass
+
+def bandpower(samples, samplerate=1, band=passband(), normalise_bandsize=True):
+    fft, freqs = get_freq_spec(samples, samplerate)
+
+    bandmask = bandpass_mask(freqs, band=band)
+
+    if normalise_bandsize:
+        bins = np.count_nonzero(bandmask, axis=-1)
+    else:
+        bins = 1
+
+    power = np.sum(np.abs(fft[bandmask])**2)
+
+    return power/bins
+
+def signal_to_noise(samples, noise, samplerate=1, signal_band=passband(), noise_band=None):
+    if noise_band is None:
+        noise_band = signal_band
+
+    if noise is None:
+        noise = samples
+
+    noise_power = bandpower(noise, samplerate, noise_band)
+
+    signal_power = bandpower(samples, samplerate, signal_band)
+
+    return (signal_power/noise_power)**0.5
+