diff --git a/code/plot_eodform_spikehist.py b/code/plot_eodform_spikehist.py index 32892bf..ce26b21 100644 --- a/code/plot_eodform_spikehist.py +++ b/code/plot_eodform_spikehist.py @@ -21,7 +21,7 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) interspikeintervals = np.diff(spikes) fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor)) -plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue') +plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue') plt.xlabel("time [ms]", fontsize = 22) plt.xticks(fontsize = 18) plt.ylabel("number of \n interspikeintervals", fontsize = 22) @@ -83,10 +83,10 @@ plt.yticks(fontsize=18) ax1.spines['top'].set_visible(False) ax2 = ax1.twinx() -ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5) +ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='navy', alpha=0.5) ax2.plot(time_axis, mu_eod, color='black', lw=2) ax2.set_ylabel('voltage [mV]', fontsize=22) -ax2.tick_params(axis='y', labelcolor='darkblue') +ax2.tick_params(axis='y', labelcolor='navy') ax2.spines['top'].set_visible(False) plt.yticks(fontsize=18) diff --git a/code/repetition_firingrate.py b/code/repetition_firingrate.py index f1f05a4..56abf88 100644 --- a/code/repetition_firingrate.py +++ b/code/repetition_firingrate.py @@ -8,7 +8,7 @@ from IPython import embed # define sampling rate and data path sampling_rate = 40 #kHz data_dir = "../data" -dataset = "2018-11-13-aj-invivo-1" +dataset = "2018-11-14-al-invivo-1" inch_factor = 2.54 # parameters for binning, smoothing and plotting cut_window = 60 @@ -23,8 +23,8 @@ spike_bins = np.arange(-cut_window, cut_window+1) #ms # read data from files spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) -eod = read_chirp_eod(os.path.join(data_dir, dataset)) -chirp_times = read_chirp_times(os.path.join(data_dir, dataset)) +#eod = read_chirp_eod(os.path.join(data_dir, dataset)) +#chirp_times = read_chirp_times(os.path.join(data_dir, dataset)) # make a delta f map for the quite more complicated keys df_map = map_keys(spikes) @@ -42,12 +42,16 @@ for deltaf in df_map.keys(): df_phase_time[deltaf] = {} df_phase_binary[deltaf] = {} for rep in df_map[deltaf]: + chirp_size = int(rep[-1].strip('Hz')) + # print(chirp_size) + if chirp_size == 150: + continue for phase in spikes[rep]: for idx in np.arange(num_bin): # check the phase if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: - # get spikes between 50 ms befor and after the chirp + # get spikes between 60 ms before and after the chirp spikes_to_cut = np.asarray(spikes[rep][phase]) spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)] spikes_idx = np.round(spikes_cut*sampling_rate) @@ -98,5 +102,5 @@ for df in df_phase_time.keys(): fig.tight_layout() #plt.show() #exit() - namefigure = '../figures/%s_%i_%i_firingrate.pdf' %(dataset, df, index_phase) - plt.savefig(namefigure) \ No newline at end of file + namefigure = '../figures/%s_%i_%i_firingrate.png' %(dataset, df, index_phase) + plt.savefig(namefigure) diff --git a/code/response_beat.py b/code/response_beat.py index 718ba8f..e57b1c7 100644 --- a/code/response_beat.py +++ b/code/response_beat.py @@ -1,30 +1,72 @@ import matplotlib.pyplot as plt import numpy as np -import scipy.stats as ss from read_chirp_data import * from utility import * from IPython import embed -# define sampling rate and data path -sampling_rate = 40 #kHz +# define data path and important parameters data_dir = "../data" -cut_window = 20 +sampling_rate = 40 #kHz +cut_window = 40 +cut_range = np.arange(-cut_window * sampling_rate, 0, 1) +window = 1 -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", +''' +# norm: -150, 150, 300 +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", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"] +# norm: -50 +data = ["2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1","2018-11-20-ad-invivo-1", + "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", + "2018-11-20-ai-invivo-1"] + +''' +data = ["2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", + "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", + "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1"] + +rates = {} + for dataset in data: spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) df_map = map_keys(spikes) print(dataset) for df in df_map.keys(): - beat_duration = 1/df + ''' + if df == 50: + pass + else: + continue + ''' + + print(df) + rep_rates = [] + beat_duration = int(abs(1 / df) * 1000) beat_window = 0 - while beat_window + beat_duration <= cut_window: + while beat_window + beat_duration <= cut_window/2: beat_window = beat_window + beat_duration for rep in df_map[df]: for phase in spikes[rep]: - response = spikes[rep][phase] + # get spikes 40 ms before the chirp first chirp + spikes_to_cut = np.asarray(spikes[rep][phase]) + spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < 0)] + spikes_idx = np.round(spikes_cut * sampling_rate) + # also save as binary, 0 no spike, 1 spike + binary_spikes = np.isin(cut_range, spikes_idx) * 1 + smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate) + train = smoothed_data[window:beat_window+window] + rep_rates.append(np.std(train)) break - #cut = response[response[]] + df_rate = np.mean(rep_rates) + if df in rates.keys(): + rates[df].append(df_rate) + else: + rates[df] = [df_rate] +fig, ax = plt.subplots() +for i, k in enumerate(sorted(rates.keys())): + ax.plot(np.ones(len(rates[k]))*k, rates[k], 'o') +#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1)) +fig.tight_layout() +plt.show() diff --git a/code/spikes_analysis.py b/code/spikes_analysis.py index 1388074..9eb27e4 100644 --- a/code/spikes_analysis.py +++ b/code/spikes_analysis.py @@ -71,7 +71,7 @@ for dataset in data: # check the phase if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: - # get spikes between 50 ms before and after the chirp + # get spikes between 40 ms before and after the chirp spikes_to_cut = np.asarray(spikes[rep][phase]) spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)] spikes_idx = np.round(spikes_cut*sampling_rate) diff --git a/code/stimulus_chirp.py b/code/stimulus_chirp.py index ad9336b..f45827c 100644 --- a/code/stimulus_chirp.py +++ b/code/stimulus_chirp.py @@ -40,8 +40,8 @@ ax1 = fig.add_subplot(211) plt.yticks(fontsize=18) ax2 = fig.add_subplot(212, sharex=ax1) plt.setp(ax1.get_xticklabels(), visible=False) -ax1.plot(time*1000, signal, color = 'royalblue', lw = 1) -ax2.plot(time*1000, freq, color = 'royalblue', lw = 3) +ax1.plot(time*1000, signal, color = 'midnightblue', lw = 1) +ax2.plot(time*1000, freq, color = 'midnightblue', lw = 3) ax1.set_ylabel("field [mV]", fontsize = 22)