else: fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Test leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2])
if noise_power == None: fig_title = 'IAF Input Signal with no Noise'; else: fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power; print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) b = 3.5 # bias d = 0.7 # threshold R = 10.0 # resistance C = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b, d, R, C) except ValueError('reconstruction condition not satisfied'): sys.exit() M = 5 # number of bins for fast decoding algorithm L = 5 # number of recursions for recursive decoding algorithm # Test leaky algorithms: output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s = func_timer(iaf.iaf_encode)(u, dt, b, d, R, C) pl.plot_encoded(t, u, s, fig_title, output_name + str(output_count) + output_ext)
noise_power = None if noise_power == None: fig_title = 'IAF Input Signal with no Noise' else: fig_title = 'IAF Input Signal with %d dB of Noise' % noise_power print fig_title u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) b = 3.5 # bias d = 0.7 # threshold R = 10.0 # resistance C = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b, d, R, C) except ValueError('reconstruction condition not satisfied'): sys.exit() M = 5 # number of bins for fast decoding algorithm L = 5 # number of recursions for recursive decoding algorithm # Test leaky algorithms: output_count += 1 fig_title = 'Signal Encoded Using Leaky IAF Encoder' print fig_title s = func_timer(iaf.iaf_encode)(u, dt, b, d, R, C) pl.plot_encoded(t, u, s, fig_title, output_name + str(output_count) + output_ext)
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power) pl.plot_signal(t, u, fig_title, output_name + str(output_count) + output_ext) # Trigonometric polynomial order: M = 32 # Test leaky IAF algorithms: b1 = 3.5 # bias d1 = 0.7 # threshold R1 = 10.0 # resistance C1 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b1, d1, R1, C1) except ValueError('reconstruction condition not satisfied'): sys.exit() b2 = 3.4 # bias d2 = 0.8 # threshold R2 = 9.0 # resistance C2 = 0.01 # capacitance try: iaf.iaf_recoverable(u, bw, b2, d2, R2, C2) except ValueError('reconstruction condition not satisfied'): sys.exit() b_list = np.array([b1, b2]) d_list = np.array([d1, d2])