Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio
This commit is contained in:
efish
commit
5875657ca9
@ -8,45 +8,48 @@ from IPython import embed
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data_dir = "../data"
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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", "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", "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", "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")
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dataset = "2018-11-13-ah-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", "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", "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", "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"]
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#for dataset in data:
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for dataset in data:
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print(dataset)
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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df_map = map_keys(eod)
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sort_df = sorted(df_map.keys())
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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df_map = map_keys(eod)
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sort_df = sorted(df_map.keys())
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chirp_eod_plot(df_map, eod, times)
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eods = chirp_eod_plot(df_map, eod, times)
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plt.show()
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plt.close('all')
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chirp_mods = []
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beat_mods = []
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for i in sort_df:
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freq = list(df_map[i])
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ls_mod, beat_mod = cut_chirps(freq, eod, times)
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chirp_mods.append(ls_mod)
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beat_mods.append(beat_mod)
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chirp_mods = {}
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beat_mods = []
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for i in sort_df:
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chirp_mods[i] = []
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freq = list(df_map[i])
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ls_mod, beat_mod = cut_chirps(freq, eod, times)
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chirp_mods[i].append(ls_mod)
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beat_mods.append(beat_mod)
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#Chirps einer Phase zuordnen - zusammen plotten
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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#plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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dct_phase = plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
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plt.show()
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plt.close('all')
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'''
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#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
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name = str(dataset.strip('invivo-1'))
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name = str(dataset.replace('-invivo-1', ''))
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print('saving ../results/Chirpcut/Cc_' + name + '.dat')
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f = open('../results/Chirpcut/Cc_' + name + '.dat' , 'w')
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f.write(str(sort_df))
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f.write(str(df_map))
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@ -56,3 +59,4 @@ for dataset in data:
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#f.write(str(chirp_mods))
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#f.write(str(beat_mods))
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f.close()
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'''
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@ -8,14 +8,16 @@ from IPython import embed #Funktionen importieren
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data_dir = "../data"
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dataset = "2018-11-09-aa-invivo-1"
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#data = ("2018-11-09-aa-invivo-1", "2018-11-09-ab-invivo-1", "2018-11-09-ac-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ab-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-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", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-aj-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", "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")
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time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
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zeit = np.asarray(time)
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plt.plot(zeit[0:1000], eod[0:1000])
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plt.title('A.lepto EOD')#Plottitelk
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plt.xlabel('time [ms]', fontsize = 12)#Achsentitel
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plt.ylabel('amplitude[mv]', fontsize = 12)#Achsentitel
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plt.xticks(fontsize = 12)
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plt.yticks(fontsize = 12)
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plt.title('A.lepto EOD', fontsize = 18)#Plottitelk
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plt.xlabel('time [ms]', fontsize = 16)#Achsentitel
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plt.ylabel('amplitude[mv]', fontsize = 16)#Achsentitel
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plt.xticks(fontsize = 14)
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plt.yticks(fontsize = 14)
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plt.show()
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@ -3,7 +3,7 @@ from read_chirp_data import *
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from func_spike import *
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import matplotlib.pyplot as plt
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import numpy as np
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from IPython import embed #Funktionen imposrtieren
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from IPython import embed #Funktionen importieren
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@ -11,77 +11,79 @@ data_dir = "../data"
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data_base = ("2018-11-09-ab-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ab-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-af-invivo-1", "2018-11-13-ag-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", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-aj-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", "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")
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data_chirps = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "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", "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", "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")
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dataset = "2018-11-13-ad-invivo-1"
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'''
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for dataset in data_base:
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#for dataset in data_base:
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print(dataset)
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spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
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spike_iv = np.diff(spike_times)
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x = np.arange(0.001, 0.01, 0.0001)
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plt.hist(spike_iv,x)
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spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
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spike_iv = np.diff(spike_times)
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x = np.arange(0.001, 0.01, 0.0001)
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plt.hist(spike_iv,x)
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mu = np.mean(spike_iv)
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sigma = np.std(spike_iv)
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cv = sigma/mu
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mu = np.mean(spike_iv)
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sigma = np.std(spike_iv)
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cv = sigma/mu
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plt.title('A.lepto ISI Histogramm', fontsize = 14)
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plt.xlabel('duration ISI[ms]', fontsize = 12)
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plt.ylabel('number of ISI', fontsize = 12)
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plt.title('A.lepto ISI Histogramm', fontsize = 18)
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plt.xlabel('duration ISI[ms]', fontsize = 16)
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plt.ylabel('number of ISI', fontsize = 16)
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plt.xticks(fontsize = 12)
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plt.yticks(fontsize = 12)
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'''
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plt.xticks(fontsize = 14)
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plt.yticks(fontsize = 14)
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plt.show()
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for dataset in data_chirps:
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#for dataset in data_chirps:
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#Nyquist-Theorem Plot:
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print(dataset)
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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plt.figure()
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ls_mean = plot_df_spikes(sort_df, dct_rate)
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dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
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plt.figure()
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ls_mean = plot_df_spikes(sort_df, dct_rate)
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plt.show()
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#mittlere Feuerrate einer Frequenz auf Frequenz:
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plt.figure()
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plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
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plt.scatter(np.arange(0,len(ls_mean),1), np.ones(len(ls_mean))*over_r)
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plt.title('Mean firing rate of a cell for a range of frequency differences')
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plt.xticks(np.arange(1,len(sort_df),1), (sort_df))
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plt.xlabel('Range of frequency differences [Hz]')
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plt.ylabel('Mean firing rate of the cell')
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plt.figure()
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plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
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plt.scatter(np.arange(0,len(ls_mean),1), np.ones(len(ls_mean))*over_r, color = 'green')
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plt.title('Mean firing rate of a cell for a range of frequency differences', fontsize = 18)
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plt.xticks(np.arange(1,len(sort_df),1), (sort_df))
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plt.xlabel('Range of frequency differences [Hz]', fontsize = 16)
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plt.ylabel('Mean firing rate of the cell', fontsize = 16)
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plt.tick_params(axis='both', which='major', labelsize = 14)
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plt.show()
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#Adaption der Zellen:
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#wie viel Prozent der Anfangsrate macht die Adaption von Zellen aus?
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adapt = adaptation_df(sort_df, dct_rate)
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plt.figure()
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plt.boxplot(adapt)
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plt.title('Adaptation of cell firing rate during a trial')
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plt.xlabel('Cell')
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plt.ylabel('Adaptation size [Hz]')
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adapt = adaptation_df(sort_df, dct_rate)
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plt.figure()
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plt.boxplot(adapt)
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plt.title('Adaptation of cell firing rate during a trial', fontsize = 18)
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plt.xlabel('Cell', fontsize = 16)
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plt.ylabel('Adaptation size [Hz]', fontsize = 16)
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plt.tick_params(axis='both', which='major', labelsize = 14)
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plt.show()
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'''
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#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
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name = str(dataset.strip('invivo-1'))
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name = str(dataset.replace('-invivo-1', ''))
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f = open('../results/Nyquist/Ny_' + name + '.txt' , 'w')
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f.write(str(sort_df))
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f.write(str(df_map))
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@ -91,3 +93,4 @@ for dataset in data_chirps:
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f.write(str(over_r))
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f.write(str(adapt))
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f.close()
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'''
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@ -21,23 +21,21 @@ def chirp_eod_plot(df_map, eod, times):
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if idx <= 3:
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axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
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elif 4<= idx <= 7:
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axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
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elif 8<= idx <= 11:
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axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
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else:
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axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
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axs[0,1].set_ylabel('Amplitude [mV]')
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axs[1,0].set_xlabel('Time [ms]')
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fig.suptitle('EOD for chirps', fontsize = 16)
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axs[0,0].set_ylabel('Amplitude [mV]')
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axs[0,1].set_xlabel('Amplitude [mV]')
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axs[1,0].set_xlabel('Time [ms]')
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axs[1,1].set_xlabel('Time [ms]')
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plt.close()
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@ -60,15 +58,17 @@ def cut_chirps(freq, eod, times):
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ls_beat.extend(beat_cut)
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beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp
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plt.figure()
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plt.scatter(np.arange(0,len(ls_mod),1), ls_mod)
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plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))*beat_mod, color = 'violet')
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plt.close()
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#plt.figure()
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#plt.scatter(np.arange(0,len(ls_mod),1), ls_mod)
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#plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))*beat_mod, color = 'violet')
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return(ls_mod, beat_mod)
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def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
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def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
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plt.figure()
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dct_phase = {}
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num_bin = 12
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||||
@ -80,8 +80,12 @@ def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
|
||||
for k in freq:
|
||||
for phase in chirp_spikes[k]:
|
||||
dct_phase[i].append(phase[1])
|
||||
|
||||
plt.scatter(dct_phase[i], ls_mod[i])
|
||||
plt.title('Change of std depending on the phase where the chirp occured')
|
||||
plt.close()
|
||||
|
||||
for i in sort_df:
|
||||
plt.scatter(dct_phase[i], chirp_mods[i], label = i)
|
||||
plt.title('Change of std depending on the phase where the chirp occured')
|
||||
plt.xlabel('Phase')
|
||||
plt.ylabel('Standard deviation of the amplitude modulation')
|
||||
plt.legend()
|
||||
return(dct_phase)
|
||||
|
||||
|
||||
@ -50,9 +50,10 @@ def plot_df_spikes(sort_df, dct_rate):
|
||||
plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
|
||||
|
||||
plt.legend()
|
||||
plt.title('Firing rate of the cell for all trials, sorted by df')
|
||||
plt.xlabel('# of trials')
|
||||
plt.ylabel('Instant firing rate of the cell')
|
||||
plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 18)
|
||||
plt.xlabel('# of trials', fontsize = 16)
|
||||
plt.ylabel('Instant firing rate of the cell', fontsize = 16)
|
||||
plt.tick_params(axis='both', which='major', labelsize = 14)
|
||||
return(ls_mean)
|
||||
|
||||
|
||||
|
||||
44
code/order_eff.py
Normal file
44
code/order_eff.py
Normal file
@ -0,0 +1,44 @@
|
||||
from read_chirp_data import *
|
||||
from func_spike import *
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed #Funktionen importieren
|
||||
|
||||
|
||||
|
||||
|
||||
data_dir = "../data"
|
||||
data_chirps = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "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", "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", "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_rate_dict = {}
|
||||
for dataset in data_chirps:
|
||||
|
||||
data_rate_dict[dataset] = []
|
||||
chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
|
||||
times = read_chirp_times(os.path.join(data_dir, dataset))
|
||||
eod = read_chirp_eod(os.path.join(data_dir, dataset))
|
||||
df_map = map_keys(chirp_spikes)
|
||||
|
||||
|
||||
for i in df_map.keys():
|
||||
freq = list(df_map[i])
|
||||
k = freq[0]
|
||||
phase = list(chirp_spikes[k].keys())[0]
|
||||
|
||||
spikes = chirp_spikes[k][phase]
|
||||
rate = len(spikes)/ 1.2
|
||||
data_rate_dict[dataset].append(rate)
|
||||
|
||||
|
||||
for dataset in data_rate_dict:
|
||||
plt.plot(data_rate_dict[dataset])
|
||||
plt.title('Test for sequence effects', fontsize = 20)
|
||||
plt.xlabel('Number of stimulus presentations', fontsize = 18)
|
||||
plt.ylabel('Firing rates of cells', fontsize = 18)
|
||||
plt.tick_params(axis='both', which='major', labelsize = 16)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
@ -1,25 +0,0 @@
|
||||
from read_baseline_data import *
|
||||
from utility import *
|
||||
#import nix_helpers as nh
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed #Funktionen importieren
|
||||
|
||||
|
||||
#Zeitpunkte einer EOD über Zero-crossings finden, die in einer Steigung liegen
|
||||
data_dir = "../data"
|
||||
dataset = "2018-11-09-ad-invivo-1"
|
||||
time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
||||
spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
|
||||
print(len(spike_times))
|
||||
|
||||
eod_times = zero_crossing(eod,time)
|
||||
eod_durations = np.diff(eod_times)
|
||||
print(len(spike_times))
|
||||
print(len(eod_durations))
|
||||
|
||||
#for st in spike_times:
|
||||
#et = eod_times[eod_times < st]
|
||||
#dt = st - et
|
||||
|
||||
#vs = vector_strength(spike_times, eod_durations)
|
||||
Loading…
Reference in New Issue
Block a user