200 lines
8.5 KiB
Python
200 lines
8.5 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import scipy.stats as ss
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from read_chirp_data import *
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from utility import *
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from IPython import embed
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# define sampling rate and data path
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sampling_rate = 40 #kHz
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data_dir = "../data"
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#dataset = "2018-11-13-al-invivo-1"
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'''
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data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1",
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"2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
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"2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1",
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"2018-11-14-ad-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1",
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"2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1",
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"2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1",
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"2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1",
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"2018-11-20-ai-invivo-1"]
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'''
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data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
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"2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
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# parameters for binning, smoothing and plotting
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cut_window = 20
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#cut_window_csi = 20 #ms
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#cut_window_plot = 50 #ms
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chirp_duration = 14 #ms
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neuronal_delay = 5 #ms
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chirp_start = int((-chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
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chirp_end = int((chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
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number_bins = 12
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window = 1 #ms
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time_axis = np.arange(-cut_window*2, cut_window*2, 1/sampling_rate) #steps
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spike_bins = np.arange(-cut_window*2, cut_window*2) #ms
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# read data from files
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#spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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#eod = read_chirp_eod(os.path.join(data_dir, dataset))
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#chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
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# make a delta f map for the quite more complicated keys
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#df_map = map_keys(spikes)
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# differentiate between phases
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phase_vec = np.arange(0, 1 + 1 / number_bins, 1 / number_bins)
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cut_range = np.arange(-cut_window*2*sampling_rate, cut_window*2*sampling_rate, 1)
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# make dictionaries for spiketimes
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df_phase_time = {}
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df_phase_binary = {}
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#embed()
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#exit()
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for dataset in data:
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(spikes)
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print(dataset)
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# iterate over delta f, repetition, phases and a single chirp
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for deltaf in df_map.keys():
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df_phase_time[deltaf] = {}
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df_phase_binary[deltaf] = {}
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for rep in df_map[deltaf]:
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chirp_size = int(rep[-1].strip('Hz'))
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#print(chirp_size)
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if chirp_size == 150:
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continue
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for phase in spikes[rep]:
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for idx in np.arange(number_bins):
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# check the phase
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if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
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# get spikes between 40 ms before and after the chirp
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spikes_to_cut = np.asarray(spikes[rep][phase])
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spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)]
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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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# add the spikes to the dictionaries with the correct df and phase
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if idx in df_phase_time[deltaf].keys():
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df_phase_time[deltaf][idx].append(spikes_cut)
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df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
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else:
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df_phase_time[deltaf][idx] = [spikes_cut]
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df_phase_binary[deltaf][idx] = binary_spikes
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# make dictionaries for csi and beat
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csi_trains = {}
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csi_rates = {}
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beat = {}
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# for plotting and calculating iterate over delta f and phases
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for df in df_phase_time.keys():
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csi_trains[df] = []
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csi_rates[df] = []
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beat[df] = []
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beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
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beat_window = 0
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# beat window is at most 20 ms long, multiples of beat_duration
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while beat_window+beat_duration <= cut_window*sampling_rate:
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beat_window = beat_window+beat_duration
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for phase in df_phase_time[df].keys():
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# csi calculation
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# trains for synchrony and rate
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trials_binary = df_phase_binary[df][phase]
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train_chirp = []
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train_beat = []
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#csi_spikerate = []
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for i, trial in enumerate(trials_binary):
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smoothed_trial = smooth(trial, window, 1/sampling_rate)
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train_chirp.append(smoothed_trial[chirp_start:chirp_end])
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train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
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#std_chirp = np.std(smoothed_trial[chirp_start:chirp_end])
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#std_beat = np.std(smoothed_trial[chirp_start-beat_window:chirp_start])
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#csi = (std_chirp - std_beat)/(std_chirp + std_beat)
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#csi_spikerate.append(csi)
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std_chirp = np.std(np.mean(train_chirp, axis=0))
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std_beat = np.std(np.mean(train_beat, axis=0))
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beat[df].append(std_beat)
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csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
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rcs = []
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rbs = []
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for i, train in enumerate(train_chirp):
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for j, train2 in enumerate(train_chirp):
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if i >= j:
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continue
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else:
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rc, _ = ss.pearsonr(train, train2)
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rb, _ = ss.pearsonr(train_beat[i], train_beat[j])
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rcs.append(rc)
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rbs.append(rb)
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r_train_chirp = np.mean(rcs)
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r_train_beat = np.mean(rbs)
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csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
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# add the csi to the dictionaries with the correct df and phase
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csi_trains[df].append(csi_train)
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csi_rates[df].append(np.mean(csi_spikerate))
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'''
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# plot
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plot_trials = df_phase_time[df][phase]
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plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
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# calculation
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#overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
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smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
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fig, ax = plt.subplots(2, 1, sharex=True)
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for i, trial in enumerate(plot_trials):
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ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
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ax[1].plot(time_axis, smoothed_spikes*1000)
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ax[0].set_title(df)
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ax[0].set_ylabel('repetition', fontsize=12)
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ax[1].set_xlabel('time [ms]', fontsize=12)
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ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
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plt.show()
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'''
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'''
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(csi_rates.keys())):
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ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
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#ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
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ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
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#ax.set_xticklabels(sorted(csi_rates.keys()))
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fig.tight_layout()
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plt.show()
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(csi_trains.keys())):
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ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
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#ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
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ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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fig.tight_layout()
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plt.show()
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'''
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(beat.keys())):
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ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
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ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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fig.tight_layout()
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plt.show()
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