weeeeh
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Ramona
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@ -9,7 +9,7 @@ from IPython import embed
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inch_factor = 2.54
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data_dir = '../data'
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#dataset = '2018-11-09-ad-invivo-1'
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dataset = '2018-11-14-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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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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fig.tight_layout()
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plt.show()
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plt.show()
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#plt.savefig('isis.pdf')
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exit()
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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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# 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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'''
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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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smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
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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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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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#exit()
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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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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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dataset = "2018-11-13-ah-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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@ -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-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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'''
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# parameters for binning, smoothing and plotting
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cut_window = 20
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@ -53,12 +55,12 @@ 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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#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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@ -87,12 +89,12 @@ for dataset in data:
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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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# 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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@ -168,32 +170,37 @@ for dataset in data:
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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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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))
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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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#ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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ax.plot([lower_limit, upper_limit], np.zeros(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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'''
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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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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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'''
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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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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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'''
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