weeeeh
This commit is contained in:
Ramona
parent
5cd62554fa
commit
85b359cdb6
@ -9,7 +9,7 @@ from IPython import embed
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inch_factor = 2.54
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inch_factor = 2.54
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data_dir = '../data'
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data_dir = '../data'
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#dataset = '2018-11-09-ad-invivo-1'
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#dataset = '2018-11-09-ad-invivo-1'
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dataset = '2018-11-14-al-invivo-1'
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dataset = '2018-11-13-aa-invivo-1'
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# read eod and time of baseline
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# read eod and time of baseline
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time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
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time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
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@ -29,7 +29,6 @@ ax.spines["top"].set_visible(False)
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ax.spines["right"].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.show()
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#plt.savefig('isis.pdf')
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#plt.savefig('isis.pdf')
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exit()
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exit()
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plt.savefig('isis.png')
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plt.savefig('isis.png')
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@ -13,7 +13,7 @@ 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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# norm: -150, 150, 300 aa, #ac, aj??
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# norm: -150, 150, 300 aa, #ac, aj??
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data = ["2018-11-13-al-invivo-1"]#, "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
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data = ["2018-11-13-aa-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-ak-invivo-1", "2018-11-13-al-invivo-1"]
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#"2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
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'''
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'''
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@ -67,10 +67,10 @@ for dataset in data:
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binary_spikes = np.isin(cut_range, spikes_idx) * 1
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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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smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
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train = smoothed_data[window:beat_window+window]
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train = smoothed_data[window:beat_window+window]
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norm_train = train*1000#/spikerate
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norm_train = train*1000/spikerate
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rep_rates.append(np.std(norm_train))#/spikerate)
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rep_rates.append(np.std(norm_train))#/spikerate)
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break
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break
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df_rate = np.median(rep_rates)/spikerate
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df_rate = np.mean(rep_rates)
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#embed()
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#embed()
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#exit()
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#exit()
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if df in rates.keys():
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if df in rates.keys():
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@ -8,7 +8,8 @@ from IPython import embed
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# define sampling rate and data path
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# define sampling rate and data path
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sampling_rate = 40 #kHz
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sampling_rate = 40 #kHz
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data_dir = "../data"
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data_dir = "../data"
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#dataset = "2018-11-13-al-invivo-1"
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dataset = "2018-11-13-ah-invivo-1"
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'''
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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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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-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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@ -18,9 +19,10 @@ data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-inviv
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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-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-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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"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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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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"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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'''
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# parameters for binning, smoothing and plotting
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# parameters for binning, smoothing and plotting
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cut_window = 20
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cut_window = 20
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@ -53,147 +55,152 @@ df_phase_binary = {}
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#embed()
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#embed()
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#exit()
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#exit()
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for dataset in data:
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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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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(spikes)
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df_map = map_keys(spikes)
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print(dataset)
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print(dataset)
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# iterate over delta f, repetition, phases and a single chirp
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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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for deltaf in df_map.keys():
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df_phase_time[deltaf] = {}
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df_phase_time[deltaf] = {}
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df_phase_binary[deltaf] = {}
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df_phase_binary[deltaf] = {}
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for rep in df_map[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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chirp_size = int(rep[-1].strip('Hz'))
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#print(chirp_size)
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#print(chirp_size)
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if chirp_size == 150:
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if chirp_size == 150:
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continue
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continue
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for phase in spikes[rep]:
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for phase in spikes[rep]:
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for idx in np.arange(number_bins):
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for idx in np.arange(number_bins):
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# check the phase
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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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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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# 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_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_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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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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# 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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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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# 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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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_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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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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else:
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rc, _ = ss.pearsonr(train, train2)
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df_phase_time[deltaf][idx] = [spikes_cut]
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rb, _ = ss.pearsonr(train_beat[i], train_beat[j])
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df_phase_binary[deltaf][idx] = binary_spikes
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rcs.append(rc)
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rbs.append(rb)
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# make dictionaries for csi and beat
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r_train_chirp = np.mean(rcs)
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csi_trains = {}
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r_train_beat = np.mean(rbs)
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csi_rates = {}
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beat = {}
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csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_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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# add the csi to the dictionaries with the correct df and phase
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csi_trains[df] = []
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csi_trains[df].append(csi_train)
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csi_rates[df] = []
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csi_rates[df].append(np.mean(csi_spikerate))
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beat[df] = []
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beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
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'''
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beat_window = 0
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# plot
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# beat window is at most 20 ms long, multiples of beat_duration
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plot_trials = df_phase_time[df][phase]
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while beat_window+beat_duration <= cut_window*sampling_rate:
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plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
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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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# calculation
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#overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
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# csi calculation
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# trains for synchrony and rate
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smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
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trials_binary = df_phase_binary[df][phase]
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fig, ax = plt.subplots(2, 1, sharex=True)
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train_chirp = []
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for i, trial in enumerate(plot_trials):
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train_beat = []
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ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
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#csi_spikerate = []
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ax[1].plot(time_axis, smoothed_spikes*1000)
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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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ax[0].set_title(df)
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train_chirp.append(smoothed_trial[chirp_start:chirp_end])
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ax[0].set_ylabel('repetition', fontsize=12)
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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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ax[1].set_xlabel('time [ms]', fontsize=12)
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#std_beat = np.std(smoothed_trial[chirp_start-beat_window:chirp_start])
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ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
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#csi = (std_chirp - std_beat)/(std_chirp + std_beat)
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plt.show()
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#csi_spikerate.append(csi)
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'''
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std_chirp = np.std(np.mean(train_chirp, axis=0))
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'''
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std_beat = np.std(np.mean(train_beat, axis=0))
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fig, ax = plt.subplots()
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beat[df].append(std_beat)
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for i, k in enumerate(sorted(csi_rates.keys())):
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csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
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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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rcs = []
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ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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rbs = []
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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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for i, train in enumerate(train_chirp):
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#ax.set_xticklabels(sorted(csi_rates.keys()))
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for j, train2 in enumerate(train_chirp):
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fig.tight_layout()
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if i >= j:
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plt.show()
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continue
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else:
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fig, ax = plt.subplots()
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rc, _ = ss.pearsonr(train, train2)
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for i, k in enumerate(sorted(csi_trains.keys())):
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rb, _ = ss.pearsonr(train_beat[i], train_beat[j])
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ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
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rcs.append(rc)
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#ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
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rbs.append(rb)
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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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r_train_chirp = np.mean(rcs)
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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r_train_beat = np.mean(rbs)
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fig.tight_layout()
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plt.show()
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csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat)
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'''
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# add the csi to the dictionaries with the correct df and phase
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fig, ax = plt.subplots()
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csi_trains[df].append(csi_train)
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for i, k in enumerate(sorted(beat.keys())):
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csi_rates[df].append(np.mean(csi_spikerate))
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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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'''
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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# plot
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fig.tight_layout()
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plot_trials = df_phase_time[df][phase]
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plt.show()
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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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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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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]))*k, 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))
|
||||||
|
ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
|
||||||
|
#ax.set_xticklabels(sorted(csi_rates.keys()))
|
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|
fig.tight_layout()
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
'''
|
||||||
|
fig, ax = plt.subplots()
|
||||||
|
for i, k in enumerate(sorted(csi_trains.keys())):
|
||||||
|
ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
|
||||||
|
#ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
|
||||||
|
ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
|
||||||
|
ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
|
||||||
|
#ax.set_xticklabels(sorted(csi_trains.keys()))
|
||||||
|
fig.tight_layout()
|
||||||
|
plt.show()
|
||||||
|
'''
|
||||||
|
|
||||||
|
'''
|
||||||
|
fig, ax = plt.subplots()
|
||||||
|
for i, k in enumerate(sorted(beat.keys())):
|
||||||
|
ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
|
||||||
|
ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
|
||||||
|
#ax.set_xticklabels(sorted(csi_trains.keys()))
|
||||||
|
fig.tight_layout()
|
||||||
|
plt.show()
|
||||||
|
'''
|
||||||
Loading…
Reference in New Issue
Block a user