finally
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Ramona
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@ -12,67 +12,78 @@ cut_window = 100
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cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
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cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
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window = 1
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window = 1
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inch_factor = 2.54
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inch_factor = 2.54
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#dataset = "2018-11-13-ad-invivo-1"
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datasets = ["2018-11-13-ad-invivo-1", "2018-11-13-aj-invivo-1", \
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#dataset = "2018-11-13-aj-invivo-1"
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"2018-11-13-ak-invivo-1", "2018-11-14-ad-invivo-1"]
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#dataset = "2018-11-13-ak-invivo-1" #al
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# dataset = "2018-11-20-af-invivo-1"
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#dataset = "2018-11-14-ad-invivo-1"
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dataset = "2018-11-20-af-invivo-1"
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base_spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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fig = plt.figure()
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base_spikes = base_spikes[1000:2000]
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# figsize=(5 / inch_factor, 2.5 / inch_factor))
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spikerate = len(base_spikes) / base_spikes[-1]
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fig.set_size_inches((35/inch_factor, 15/inch_factor))
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print(spikerate)
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axes = []
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axes.append(fig.add_subplot(221))
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axes.append(fig.add_subplot(222))
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axes.append(fig.add_subplot(223))
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axes.append(fig.add_subplot(224))
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# read spikes during chirp stimulation
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for dataset, ax in zip(datasets, axes):
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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base_spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(spikes)
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base_spikes = base_spikes[1000:2000]
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spikerate = len(base_spikes) / base_spikes[-1]
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print(spikerate)
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rates = {}
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# read spikes during chirp stimulation
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# iterate over df
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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for deltaf in df_map.keys():
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df_map = map_keys(spikes)
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rates[deltaf] = {}
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beat_duration = int(abs(1 / deltaf) * 1000)
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beat_window = 0
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while beat_window + beat_duration <= cut_window/2:
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beat_window = beat_window + beat_duration
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for x, repetition in enumerate(df_map[deltaf]):
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for phase in spikes[repetition]:
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# get spikes some ms before the chirp first chirp
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spikes_to_cut = np.asarray(spikes[repetition][phase])
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spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < 0)]
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spikes_idx = np.round(spikes_cut * sampling_rate)
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# also save as binary, 0 no spike, 1 spike
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binary_spikes = np.isin(cut_range, spikes_idx) * 1
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smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
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#train = smoothed_data[window*sampling_rate:beat_window*sampling_rate+window*sampling_rate]
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modulation = np.std(smoothed_data)
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rates[deltaf][x] = modulation
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break
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plt.close()
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rates = {}
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fig = plt.figure()
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# iterate over df
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#figsize=(5 / inch_factor, 2.5 / inch_factor))
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for deltaf in df_map.keys():
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fig.set_size_inches((5, 2.5))
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rates[deltaf] = {}
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ax = fig.add_subplot(111)
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beat_duration = int(abs(1 / deltaf) * 1000)
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for i, df in enumerate(sorted(rates.keys())):
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beat_window = 0
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max_rep = len(sorted(rates[df].keys()))-1
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while beat_window + beat_duration <= cut_window/2:
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for j, rep in enumerate(rates[df].keys()):
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beat_window = beat_window + beat_duration
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if j == max_rep:
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for x, repetition in enumerate(df_map[deltaf]):
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farbe = 'royalblue'
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for phase in spikes[repetition]:
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gro = 8
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# get spikes some ms before the chirp first chirp
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else:
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spikes_to_cut = np.asarray(spikes[repetition][phase])
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farbe = 'k'
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spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < 0)]
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gro = 6
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spikes_idx = np.round(spikes_cut * sampling_rate)
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ax.plot(df, rates[df][rep], marker='o', color=farbe, ms=gro)
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# also save as binary, 0 no spike, 1 spike
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binary_spikes = np.isin(cut_range, spikes_idx) * 1
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smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
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modulation = np.std(smoothed_data)
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rates[deltaf][x] = modulation
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break
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#ax.set_xlabel('difference frequency [Hz]', fontsize=20)
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for i, df in enumerate(sorted(rates.keys())):
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ax.set_ylabel('firing modulation', fontsize=16)
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max_rep = len(sorted(rates[df].keys()))-1
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ax.set_xlabel('$\Delta$f [Hz]', fontsize=16)
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for j, rep in enumerate(rates[df].keys()):
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if j == max_rep:
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farbe = 'royalblue'
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gro = 8
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else:
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farbe = 'k'
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gro = 6
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ax.plot(df, rates[df][rep], marker='o', color=farbe, ms=gro)
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ax.set_xlim(-450, 800)
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ax.set_ylim(0.1, 0.3)
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ax.set_xticks(np.arange(-400, 810, 100))
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ax.yaxis.set_tick_params(labelsize=18)
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ax.xaxis.set_tick_params(labelsize=18)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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ax.yaxis.set_tick_params(labelsize=14)
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axes[0].set_ylabel('Firing rate modulation', fontsize=22)
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ax.xaxis.set_tick_params(labelsize=14)
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axes[0].yaxis.set_label_coords(-0.15, -.125)
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axes[2].set_xlabel('$\Delta$f [Hz]', fontsize=22)
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axes[3].set_xlabel('$\Delta$f [Hz]', fontsize=22)
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axes[1].set_yticklabels([])
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axes[3].set_yticklabels([])
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axes[0].set_xticklabels([])
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axes[1].set_xticklabels([])
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axes[2].set_xticklabels(['-400', '', '-200', '', '0', '', '200', '', '400', '', '600', '', '800'], rotation=45)
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axes[3].set_xticklabels(['-400', '', '-200', '', '0', '', '200', '', '400', '', '600', '', '800'], rotation=45)
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ax.spines["top"].set_visible(False)
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fig.subplots_adjust(left=0.09, bottom=0.175, right=0.975, top=0.95)
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ax.spines["right"].set_visible(False)
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fig.tight_layout()
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fig.savefig('spikes_beat_20af.png')
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fig.savefig('spikes_beat_20af.png')
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@ -93,10 +93,21 @@ for df in df_phase_binary.keys():
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upper_limit = np.max(sorted(csi_rates.keys()))+30
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upper_limit = np.max(sorted(csi_rates.keys()))+30
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lower_limit = np.min(sorted(csi_rates.keys()))-30
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lower_limit = np.min(sorted(csi_rates.keys()))-30
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fig, ax = plt.subplots()
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inch_factor = 2.54
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
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ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
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for i, df in enumerate(sorted(csi_rates.keys())):
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for i, df in enumerate(sorted(csi_rates.keys())):
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for j, phase in enumerate(sorted(csi_rates[df].keys())):
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for j, phase in enumerate(sorted(csi_rates[df].keys())):
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ax.plot(df, csi_rates[df][phase], 'o', color=colors[j], ms=sizes[j])
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ax.plot(df, csi_rates[df][phase], 'o', color=colors[j], ms=sizes[j])
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plt.xlabel("$\Delta$f", fontsize = 22)
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plt.xticks(fontsize = 18)
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plt.ylabel("CSI", fontsize = 22)
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plt.yticks(fontsize = 18)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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fig.tight_layout()
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fig.tight_layout()
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plt.show()
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#plt.show()
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plt.savefig('CSI.png')
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