Add two tone and mer measure

1 files changed, 64 insertions, 3 deletions

diff --git a/src/dab_tuning_lib.py b/src/dab_tuning_lib.py
index 8faafef..e5348b0 100644
--- a/src/dab_tuning_lib.py
+++ b/src/dab_tuning_lib.py
@@ -17,7 +17,6 @@ def calc_signal_sholder_ratio(fft, sampling_rate, debug = False, debug_path="",
                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)
@@ -25,7 +24,69 @@ def calc_signal_sholder_ratio(fft, sampling_rate, debug = False, debug_path="",
         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")
+        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
+        plt.show()
         plt.clf()
 
-    return score
+    return sholder
+
+def calc_signal_sholder_peak_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]])
+
+    loss = sholder/sig
+
+
+    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')
+        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
+        plt.show()
+        plt.clf()
+
+    return loss
+
+def calc_max_in_freq_range(fft, sampling_rate, f_start, f_end, debug = False, debug_path="", suffix=""):
+    fft_size = fft.shape[0]
+    n_sig = (du.freq_to_fft_sample(f_start, fft_size, sampling_rate),
+             du.freq_to_fft_sample(f_end, fft_size, sampling_rate))
+    sig = np.max(fft[n_sig[0]:n_sig[1]])
+
+    if debug == True:
+        print(n_sig)
+        plt.plot(fft)
+        plt.plot((n_sig[0], n_sig[1]), (sig, sig), linewidth=5, color='g')
+        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
+        plt.show()
+        plt.clf()
+
+    return sig
+
+def calc_mean_in_freq_range(fft, sampling_rate, f_start, f_end, debug = False, debug_path="", suffix=""):
+    fft_size = fft.shape[0]
+    n_sig = (du.freq_to_fft_sample(f_start, fft_size, sampling_rate),
+             du.freq_to_fft_sample(f_end, fft_size, sampling_rate))
+    sig = np.mean(fft[n_sig[0]:n_sig[1]])
+
+    if debug == True:
+        print(n_sig)
+        plt.plot(fft)
+        plt.plot((n_sig[0], n_sig[1]), (sig, sig), linewidth=5, color='g')
+        if debug_path: plt.savefig(debug_path + "/" + str(loss) + suffix + ".png")
+        plt.show()
+        plt.clf()
+
+    return sig