diff --git a/capture.bin b/capture.bin
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index 0000000..4c57689
Binary files /dev/null and b/capture.bin differ
diff --git a/main.py b/main.py
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+++ b/main.py
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+import numpy as np
+import matplotlib.pyplot as plt
+
+Fs = 1 # Hz
+N = 100 # number of points to simulate, and our FFT size
+
+t = np.arange(N) # because our sample rate is 1 Hz
+s = np.sin(0.15*2*np.pi*t)
+#s = s * np.hamming(100)
+S = np.fft.fftshift(np.fft.fft(s))
+S_mag = np.abs(S)
+S_phase = np.angle(S)
+f = np.arange(Fs/-2, Fs/2, Fs/N)
+#plt.figure(0)
+#plt.plot(f, S_mag,'.-')
+#plt.figure(1)
+#plt.plot(f, S_phase,'.-')
+#plt.show()
+
+
+# simulate the signal above, or use your own signal
+
+x = 20
+
+fft_size = 1024
+sample_rate = 512
+num_rows = len(s) // fft_size # // is an integer division which rounds down
+spectrogram = np.zeros((num_rows, fft_size))
+for i in range(num_rows):
+    spectrogram[i,:] = 10*np.log10(np.abs(np.fft.fftshift(np.fft.fft(x[i*fft_size:(i+1)*fft_size])))**2)
+
+plt.imshow(spectrogram, aspect='auto', extent = [sample_rate/-2/1e6, sample_rate/2/1e6, len(s)/sample_rate, 0])
+plt.xlabel("Frequency [MHz]")
+plt.ylabel("Time [s]")
+plt.show()
diff --git a/rtl.py b/rtl.py
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--- /dev/null
+++ b/rtl.py
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+from rtlsdr import RtlSdr
+import numpy as np
+import matplotlib.pyplot as plt
+
+sdr = RtlSdr()
+sdr.sample_rate = 2.048e6 # Hz
+sdr.center_freq = 100e6   # Hz
+sdr.freq_correction = 60  # PPM
+print(sdr.valid_gains_db)
+sdr.gain = 49.6
+print(sdr.gain)
+
+x = sdr.read_samples(4096)
+sdr.close()
+
+plt.plot(x.real)
+plt.plot(x.imag)
+plt.legend(["I", "Q"])
+plt.savefig("../_images/rtlsdr-gain.svg", bbox_inches='tight')
+plt.show()
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diff --git a/sec.py b/sec.py
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+++ b/sec.py
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+import numpy as np
+import matplotlib.pyplot as plt
+
+sample_rate = 1e6
+
+# Generate tone plus noise
+t = np.arange(1024*1000)/sample_rate # time vector
+f = 50e3 # freq of tone
+x = np.sin(2*np.pi*f*t) 
+
+# simulate the signal above, or use your own signal
+
+fft_size = 1024
+num_rows = len(x) // fft_size # // is an integer division which rounds down
+spectrogram = np.zeros((num_rows, fft_size))
+for i in range(num_rows):
+    spectrogram[i,:] = 10*np.log10(np.abs(np.fft.fftshift(np.fft.fft(x[i*fft_size:(i+1)*fft_size])))**2)
+
+plt.imshow(spectrogram, aspect='auto', extent = [sample_rate/-2/1e6, sample_rate/2/1e6, len(x)/sample_rate, 0])
+plt.xlabel("Frequency [MHz]")
+plt.ylabel("Time [s]")
+plt.show()
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