1 files changed, 31 insertions, 0 deletions
diff --git a/src/dab_tuning_lib.py b/src/dab_tuning_lib.py
new file mode 100644
index 0000000..8faafef
--- /dev/null
+++ b/src/dab_tuning_lib.py
@@ -0,0 +1,31 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import src.dab_util as du
+
+
+def calc_signal_sholder_ratio(fft, sampling_rate, debug = False, debug_path="", suffix=""):
+    fft_size = fft.shape[0]
+    n_sig = (du.freq_to_fft_sample(-du.c["bw"]/2., fft_size, sampling_rate),
+             du.freq_to_fft_sample( du.c["bw"]/2., fft_size, sampling_rate))
+    sig = np.mean(fft[n_sig[0]:n_sig[1]])
+
+    n_noise = (du.freq_to_fft_sample(-3000000., fft_size, sampling_rate),
+               du.freq_to_fft_sample(-2500000, fft_size, sampling_rate))
+    noise = np.mean(fft[n_noise[0]:n_noise[1]])
+
+    n_sholder = (du.freq_to_fft_sample(-1500000, fft_size, sampling_rate),
+               du.freq_to_fft_sample(-du.c["bw"]/2, fft_size, sampling_rate))
+    sholder = np.mean(fft[n_sholder[0]:n_sholder[1]])
+
+    score = -sholder
+
+    if debug == True:
+        print(n_sig, n_sholder, n_noise)
+        plt.plot(fft)
+        plt.plot((n_sig[0], n_sig[1]), (sig, sig), linewidth=5, color='g')
+        plt.plot((n_noise[0], n_noise[1]), (noise, noise), linewidth=5, color='r')
+        plt.plot((n_sholder[0], n_sholder[1]), (sholder, sholder), linewidth=5, color='y')
+        plt.savefig(debug_path + "/" + str(score) + suffix + ".png")
+        plt.clf()
+
+    return score