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code/response_beat.py
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30
code/response_beat.py
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@ -0,0 +1,30 @@
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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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cut_window = 20
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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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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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for df in df_map.keys():
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beat_duration = 1/df
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beat_window = 0
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while beat_window + beat_duration <= cut_window:
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beat_window = beat_window + beat_duration
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for rep in df_map[df]:
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for phase in spikes[rep]:
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response = spikes[rep][phase]
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break
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#cut = response[response[]]
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@ -8,14 +8,19 @@ 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 = "2018-11-14-ad-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-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-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", \
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# "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",\
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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"," 2018-11-20-ae-invivo-1", \
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# "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1")
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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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@ -31,12 +36,12 @@ 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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#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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#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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@ -48,8 +53,12 @@ df_phase_binary = {}
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#embed()
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#exit()
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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 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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@ -78,13 +87,15 @@ for deltaf in df_map.keys():
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df_phase_binary[deltaf][idx] = binary_spikes
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# make dictionaries for csi
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csi_trains = {}
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csi_rates = {}
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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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# 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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@ -110,6 +121,7 @@ for df in df_phase_time.keys():
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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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@ -156,32 +168,32 @@ for df in df_phase_time.keys():
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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(csi_rates.keys())):
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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.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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# spikerate_chirp = np.zeros(len(trials_binary))
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# spikerate_beat = np.zeros(len(trials_binary))
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# csi_trains[df][phase] = csi_train
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# csi_rates[df][phase] = csi_rate
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# csi_trains[df].append(abs(csi_train))
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# csi_rates[df].append(abs(csi_rate))
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#csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat))
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# spikerate_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end])
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# spikerate_beat[i] = np.mean(smoothed_trial[chirp_start-beat_window:chirp_start])
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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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